
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings
filterwarnings('ignore')
plt.rcParams['figure.figsize'] = (12,7)
import scipy
from scipy import stats
from scipy.stats import ttest_ind
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.formula.api import ols
from statsmodels.stats.multicomp import pairwise_tukeyhsd


data = pd.read_csv("D:/Machine Learning/Project/HousePrices.csv")


data.sample(10)


data.drop(['Id'],axis = 1,inplace = True)


data.shape

(2073, 80)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 80 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LotFrontage          1753 non-null   float64
 3   LotArea              2073 non-null   int64  
 4   Road_Type            2073 non-null   object 
 5   Alley                129 non-null    object 
 6   Property_Shape       2073 non-null   object 
 7   LandContour          2073 non-null   object 
 8   Utilities            2073 non-null   object 
 9   LotConfig            2073 non-null   object 
 10  LandSlope            2073 non-null   object 
 11  Neighborhood         2073 non-null   object 
 12  Condition1           2073 non-null   object 
 13  Condition2           2073 non-null   object 
 14  Dwelling_Type        2073 non-null   object 
 15  HouseStyle           2073 non-null   object 
 16  OverallQual          2073 non-null   int64  
 17  OverallCond          2073 non-null   int64  
 18  YearBuilt            2073 non-null   int64  
 19  YearRemodAdd         2073 non-null   int64  
 20  RoofStyle            2073 non-null   object 
 21  RoofMatl             2073 non-null   object 
 22  Exterior1st          2073 non-null   object 
 23  Exterior2nd          2073 non-null   object 
 24  MasVnrType           2059 non-null   object 
 25  MasVnrArea           2059 non-null   float64
 26  ExterQual            2073 non-null   object 
 27  ExterCond            2073 non-null   object 
 28  Foundation           2073 non-null   object 
 29  BsmtQual             2014 non-null   object 
 30  BsmtCond             2014 non-null   object 
 31  BsmtExposure         2012 non-null   object 
 32  BsmtFinType1         2014 non-null   object 
 33  BsmtFinSF1           2073 non-null   int64  
 34  BsmtFinType2         2013 non-null   object 
 35  BsmtFinSF2           2073 non-null   int64  
 36  BsmtUnfSF            2073 non-null   int64  
 37  TotalBsmtSF          2073 non-null   int64  
 38  Heating              2073 non-null   object 
 39  HeatingQC            2073 non-null   object 
 40  CentralAir           2073 non-null   object 
 41  Electrical           2072 non-null   object 
 42  1stFlrSF             2073 non-null   int64  
 43  2ndFlrSF             2073 non-null   int64  
 44  LowQualFinSF         2073 non-null   int64  
 45  GrLivArea            2073 non-null   int64  
 46  BsmtFullBath         2073 non-null   int64  
 47  BsmtHalfBath         2073 non-null   int64  
 48  FullBath             2073 non-null   int64  
 49  HalfBath             2073 non-null   int64  
 50  BedroomAbvGr         2073 non-null   int64  
 51  KitchenAbvGr         2073 non-null   int64  
 52  KitchenQual          2073 non-null   object 
 53  TotRmsAbvGrd         2073 non-null   int64  
 54  Functional           2073 non-null   object 
 55  Fireplaces           2073 non-null   int64  
 56  FireplaceQu          1085 non-null   object 
 57  GarageType           1960 non-null   object 
 58  GarageYrBlt          1960 non-null   float64
 59  GarageFinish         1960 non-null   object 
 60  GarageCars           2073 non-null   int64  
 61  GarageArea           2073 non-null   int64  
 62  GarageQual           1960 non-null   object 
 63  GarageCond           1960 non-null   object 
 64  PavedDrive           2073 non-null   object 
 65  WoodDeckSF           2073 non-null   int64  
 66  OpenPorchSF          2073 non-null   int64  
 67  EnclosedPorch        2073 non-null   int64  
 68  3SsnPorch            2073 non-null   int64  
 69  ScreenPorch          2073 non-null   int64  
 70  PoolArea             2073 non-null   int64  
 71  PoolQC               8 non-null      object 
 72  Fence                404 non-null    object 
 73  MiscFeature          80 non-null     object 
 74  MiscVal              2073 non-null   int64  
 75  MoSold               2073 non-null   int64  
 76  YrSold               2073 non-null   int64  
 77  SaleType             2073 non-null   object 
 78  SaleCondition        2073 non-null   object 
 79  Property_Sale_Price  2073 non-null   int64  
dtypes: float64(3), int64(34), object(43)
memory usage: 1.3+ MB


data = data.rename(columns = {'1stFlrSF' : 'FirstFlrSF', '2ndFlrSF' : 'SecondFlrSF', '3SsnPorch' : 'ThrSsnPorch'})


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 80 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LotFrontage          1753 non-null   float64
 3   LotArea              2073 non-null   int64  
 4   Road_Type            2073 non-null   object 
 5   Alley                129 non-null    object 
 6   Property_Shape       2073 non-null   object 
 7   LandContour          2073 non-null   object 
 8   Utilities            2073 non-null   object 
 9   LotConfig            2073 non-null   object 
 10  LandSlope            2073 non-null   object 
 11  Neighborhood         2073 non-null   object 
 12  Condition1           2073 non-null   object 
 13  Condition2           2073 non-null   object 
 14  Dwelling_Type        2073 non-null   object 
 15  HouseStyle           2073 non-null   object 
 16  OverallQual          2073 non-null   int64  
 17  OverallCond          2073 non-null   int64  
 18  YearBuilt            2073 non-null   int64  
 19  YearRemodAdd         2073 non-null   int64  
 20  RoofStyle            2073 non-null   object 
 21  RoofMatl             2073 non-null   object 
 22  Exterior1st          2073 non-null   object 
 23  Exterior2nd          2073 non-null   object 
 24  MasVnrType           2059 non-null   object 
 25  MasVnrArea           2059 non-null   float64
 26  ExterQual            2073 non-null   object 
 27  ExterCond            2073 non-null   object 
 28  Foundation           2073 non-null   object 
 29  BsmtQual             2014 non-null   object 
 30  BsmtCond             2014 non-null   object 
 31  BsmtExposure         2012 non-null   object 
 32  BsmtFinType1         2014 non-null   object 
 33  BsmtFinSF1           2073 non-null   int64  
 34  BsmtFinType2         2013 non-null   object 
 35  BsmtFinSF2           2073 non-null   int64  
 36  BsmtUnfSF            2073 non-null   int64  
 37  TotalBsmtSF          2073 non-null   int64  
 38  Heating              2073 non-null   object 
 39  HeatingQC            2073 non-null   object 
 40  CentralAir           2073 non-null   object 
 41  Electrical           2072 non-null   object 
 42  FirstFlrSF           2073 non-null   int64  
 43  SecondFlrSF          2073 non-null   int64  
 44  LowQualFinSF         2073 non-null   int64  
 45  GrLivArea            2073 non-null   int64  
 46  BsmtFullBath         2073 non-null   int64  
 47  BsmtHalfBath         2073 non-null   int64  
 48  FullBath             2073 non-null   int64  
 49  HalfBath             2073 non-null   int64  
 50  BedroomAbvGr         2073 non-null   int64  
 51  KitchenAbvGr         2073 non-null   int64  
 52  KitchenQual          2073 non-null   object 
 53  TotRmsAbvGrd         2073 non-null   int64  
 54  Functional           2073 non-null   object 
 55  Fireplaces           2073 non-null   int64  
 56  FireplaceQu          1085 non-null   object 
 57  GarageType           1960 non-null   object 
 58  GarageYrBlt          1960 non-null   float64
 59  GarageFinish         1960 non-null   object 
 60  GarageCars           2073 non-null   int64  
 61  GarageArea           2073 non-null   int64  
 62  GarageQual           1960 non-null   object 
 63  GarageCond           1960 non-null   object 
 64  PavedDrive           2073 non-null   object 
 65  WoodDeckSF           2073 non-null   int64  
 66  OpenPorchSF          2073 non-null   int64  
 67  EnclosedPorch        2073 non-null   int64  
 68  ThrSsnPorch          2073 non-null   int64  
 69  ScreenPorch          2073 non-null   int64  
 70  PoolArea             2073 non-null   int64  
 71  PoolQC               8 non-null      object 
 72  Fence                404 non-null    object 
 73  MiscFeature          80 non-null     object 
 74  MiscVal              2073 non-null   int64  
 75  MoSold               2073 non-null   int64  
 76  YrSold               2073 non-null   int64  
 77  SaleType             2073 non-null   object 
 78  SaleCondition        2073 non-null   object 
 79  Property_Sale_Price  2073 non-null   int64  
dtypes: float64(3), int64(34), object(43)
memory usage: 1.3+ MB


data.describe()


data.describe(include='O')


# Check which variables have missing values

data.isnull().sum()

Dwell_Type               0
Zone_Class               0
LotFrontage            320
LotArea                  0
Road_Type                0
                      ... 
MoSold                   0
YrSold                   0
SaleType                 0
SaleCondition            0
Property_Sale_Price      0
Length: 80, dtype: int64


sns.heatmap(data.isnull(), cmap = 'Paired_r')
plt.title('Heat Map of Nulls')

Text(0.5, 1.0, 'Heat Map of Nulls')


data.Property_Sale_Price.isnull().sum()

0


data.Property_Sale_Price.describe()

count      2073.000000
mean     180967.284129
std       78666.411112
min       34900.000000
25%      130000.000000
50%      163990.000000
75%      214000.000000
max      755000.000000
Name: Property_Sale_Price, dtype: float64


data.Property_Sale_Price.value_counts()

140000    31
135000    28
110000    23
160000    22
155000    20
          ..
412500     1
306000     1
195400     1
283463     1
250580     1
Name: Property_Sale_Price, Length: 663, dtype: int64


data.Property_Sale_Price.value_counts().sum()

2073


plt.hist(data.Property_Sale_Price)
plt.title('Hist of Property_Sale_Price')

Text(0.5, 1.0, 'Hist of Property_Sale_Price')


sns.distplot(data.Property_Sale_Price)
plt.title('Normality of Property_Sale_Price')

Text(0.5, 1.0, 'Normality of Property_Sale_Price')


sns.countplot(data.Property_Sale_Price)
plt.title('Count plot of Property_Sale_Price')

Text(0.5, 1.0, 'Count plot of Property_Sale_Price')


sns.boxplot(x = data.Property_Sale_Price, data =data, color = 'r')
plt.title('Box plot of Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of Property_Sale_Price')


IQR = data.Property_Sale_Price.quantile(.75) - data.Property_Sale_Price.quantile(.25)
IQR

84000.0


upper_limit = data.Property_Sale_Price.quantile(.75) + (1.5*IQR)
upper_limit

340000.0


lower_limit = data.Property_Sale_Price.quantile(.25) - (1.5*IQR)
lower_limit

4000.0


len(data.Property_Sale_Price[data.Property_Sale_Price > upper_limit])

78


len(data.Property_Sale_Price[data.Property_Sale_Price < lower_limit])

0


for i in np.arange(upper_limit,800000,100000):
    outliers = len(data.Property_Sale_Price[data.Property_Sale_Price > i])
    print('At a limit of :', i, 'There are', outliers, 'outliers')

At a limit of : 340000.0 There are 78 outliers
At a limit of : 440000.0 There are 21 outliers
At a limit of : 540000.0 There are 11 outliers
At a limit of : 640000.0 There are 2 outliers
At a limit of : 740000.0 There are 2 outliers


data['Property_Sale_Price'] = np.where(data['Property_Sale_Price'] > upper_limit, upper_limit, data['Property_Sale_Price'])


sns.boxplot(x = data.Property_Sale_Price, data =data, color = 'g')
plt.title('Box plot of Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of Property_Sale_Price')


sns.distplot(data.Property_Sale_Price)
plt.title('Normality of Property_Sale_Price')

Text(0.5, 1.0, 'Normality of Property_Sale_Price')


sns.violinplot(data.Property_Sale_Price, color = 'pink')
plt.title('Violinplot Of Property_Sale_Price')

Text(0.5, 1.0, 'Violinplot Of Property_Sale_Price')


data.Dwell_Type.isnull().sum()

0


data.Dwell_Type.describe()

count    2073.000000
mean       60.556199
std       159.924810
min        20.000000
25%        20.000000
50%        50.000000
75%        70.000000
max      7080.000000
Name: Dwell_Type, dtype: float64


data.Dwell_Type.value_counts()

20      722
60      431
50      205
30      133
120     114
90       91
80       78
160      78
70       77
190      42
85       34
75       19
180      14
150      13
45       13
40        7
303       1
7080      1
Name: Dwell_Type, dtype: int64


data["Dwell_Type"]=data["Dwell_Type"].replace([303,7080],[30,70])


data["Dwell_Type"].value_counts()

20     722
60     431
50     205
30     134
120    114
90      91
70      78
80      78
160     78
190     42
85      34
75      19
180     14
150     13
45      13
40       7
Name: Dwell_Type, dtype: int64


data["Dwell_Type"].value_counts().sum()

2073


sns.countplot(data.Dwell_Type)
plt.title('Count plot of Dwell_Type')

Text(0.5, 1.0, 'Count plot of Dwell_Type')


data.groupby("Dwell_Type")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Dwell_Type', data = data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq          F    PR(>F)
Dwell_Type     1.0  5.534241e+10  5.534241e+10  12.386042  0.000442
Residual    2071.0  9.253492e+12  4.468127e+09        NaN       NaN


tukey_Dwell_Type = pairwise_tukeyhsd(data.Property_Sale_Price, data.Dwell_Type, alpha = 0.05)
print(tukey_Dwell_Type)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
    20     30   -55848.157  0.001  -74810.1603 -36886.1536   True
    20     40   -2592.2465    0.9  -79154.4599  73969.9668  False
    20     45  -69230.7081 0.0028 -125642.7382 -12818.6779   True
    20     50  -32272.3831  0.001  -48226.1291 -16318.6372   True
    20     60   46685.8579  0.001   34414.9766  58956.7392   True
    20     70   -9556.0414    0.9  -33582.9241  14470.8413  False
    20     75     -60.6676    0.9  -46913.0888  46791.7537  False
    20     80  -10151.4773    0.9    -34178.36  13875.4054  False
    20     85  -33624.5995 0.0844  -69001.6136   1752.4147  False
    20     90  -43019.4224  0.001  -65444.0021 -20594.8426   True
    20    120   20274.0166 0.0512     -42.5562  40590.5894  False
    20    150   10580.8304    0.9  -45831.1998  66992.8605  False
    20    160  -40699.7081  0.001  -64726.5908 -16672.8254   True
    20    180  -76406.5323  0.001 -130803.4917 -22009.5728   True
    20    190  -50030.3418  0.001  -82028.2634 -18032.4202   True
    30     40   53255.9104 0.5812  -24902.6356 131414.4565  False
    30     45  -13382.5511    0.9  -71942.8017  45177.6995  False
    30     50   23575.7739 0.0275    1181.4036  45970.1441   True
    30     60  102534.0149  0.001   82595.0997   122472.93   True
    30     70   46292.1156  0.001   17581.8927  75002.3385   True
    30     75   55787.4894 0.0107    6369.5252 105205.4536   True
    30     80   45696.6797  0.001   16986.4568  74406.9026   True
    30     85   22223.5575 0.8191  -16487.1508  60934.2658  False
    30     90   12828.7346    0.9  -14554.6104  40212.0796  False
    30    120   76122.1736  0.001   50436.5952  101807.752   True
    30    150   66428.9874   0.01    7868.7368  124989.238   True
    30    160   15148.4489    0.9   -13561.774  43858.6718  False
    30    180  -20558.3753    0.9  -77180.0584  36063.3079  False
    30    190    5817.8152    0.9  -29831.2071  41466.8376  False
    40     45  -66638.4615 0.5286 -161145.1697  27868.2467  False
    40     50  -29680.1366    0.9 -107163.8301  47803.5569  False
    40     60   49278.1044 0.6695  -27531.8902 126088.0991  False
    40     70   -6963.7949    0.9  -86503.0421  72575.4523  False
    40     75    2531.5789    0.9   -86599.505  91662.6629  False
    40     80   -7559.2308    0.9  -87098.4779  71980.0164  False
    40     85  -31032.3529    0.9 -114702.7412  52638.0354  False
    40     90  -40427.1758    0.9 -119497.1608  38642.8092  False
    40    120   22866.2632    0.9  -55631.9144 101364.4407  False
    40    150   13173.0769    0.9  -81333.6313 107679.7851  False
    40    160  -38107.4615    0.9 -117646.7087  41431.7856  False
    40    180  -73814.2857 0.3225  -167132.183  19503.6115  False
    40    190  -47438.0952 0.8139 -129736.7449  34860.5544  False
    45     50    36958.325 0.6707  -20698.1352  94614.7851  False
    45     60  115916.5659  0.001   59168.7034 172664.4285   True
    45     70   59674.6667  0.057    -716.0319 120065.3652  False
    45     75   69170.0405 0.0821    -3389.554  141729.635  False
    45     80   59079.2308 0.0634   -1311.4678 119469.9293  False
    45     85   35606.1086  0.891  -30130.3235 101342.5407  False
    45     90   26211.2857    0.9  -33560.0047  85982.5761  False
    45    120   89504.7247  0.001   30491.9421 148517.5073   True
    45    150   79811.5385 0.0452     741.5535 158881.5235   True
    45    160      28531.0    0.9  -31859.6986  88921.6986  False
    45    180   -7175.8242    0.9   -84821.008  70469.3597  False
    45    190   19200.3663    0.9  -44780.9776  83181.7102  False
    50     60    78958.241  0.001   61854.8853  96061.5967   True
    50     70   22716.3417 0.2131   -4102.3335  49535.0169  False
    50     75   32211.7155 0.6146  -16131.8427  80555.2738  False
    50     80   22120.9058  0.253   -4697.7694   48939.581  False
    50     85   -1352.2164    0.9  -38681.6066  35977.1739  False
    50     90  -10747.0392    0.9  -36140.1884  14646.1099  False
    50    120   52546.3997  0.001   28994.0458  76098.7537   True
    50    150   42853.2135 0.4401  -14803.2466 100509.6736  False
    50    160    -8427.325    0.9  -35246.0002  18391.3502  False
    50    180  -44134.1491 0.3193  -99820.5877  11552.2895  False
    50    190  -17757.9587    0.9  -51902.0245  16386.1072  False
    60     70  -56241.8993  0.001  -81047.0168 -31436.7817   True
    60     75  -46746.5255 0.0563  -94002.7646    509.7137  False
    60     80  -56837.3352  0.001  -81642.4527 -32032.2176   True
    60     85  -80310.4573  0.001 -116220.5659 -44400.3488   True
    60     90  -89705.2802  0.001 -112961.7748 -66448.7856   True
    60    120  -26411.8413 0.0022  -47643.0893  -5180.5932   True
    60    150  -36105.0275 0.6813  -92852.8901  20642.8351  False
    60    160  -87385.5659  0.001 -112190.6835 -62580.4484   True
    60    180 -123092.3901  0.001 -177837.5448 -68347.2354   True
    60    190  -96716.1996  0.001 -129302.5427 -64129.8566   True
    70     75    9495.3738    0.9  -42078.5412  61069.2888  False
    70     80    -595.4359    0.9  -32875.6221  31684.7503  False
    70     85  -24068.5581 0.8038  -65496.2355  17359.1194  False
    70     90   -33463.381 0.0209  -64569.3467  -2357.4152   True
    70    120    29830.058 0.0464     207.7276  59452.3885   True
    70    150   20136.8718    0.9  -40253.8268  80527.5704  False
    70    160  -31143.6667  0.073  -63423.8529   1136.5195  False
    70    180  -66850.4908  0.009 -125363.2974  -8337.6843   True
    70    190  -40474.3004 0.0288  -79056.5024  -1892.0983   True
    75     80  -10090.8097    0.9  -61664.7247  41483.1053  False
    75     85  -33563.9319 0.8031   -91305.691  24177.8272  False
    75     90  -42958.7548 0.2169  -93805.9709   7888.4614  False
    75    120   20334.6842    0.9  -29618.7015  70288.0699  False
    75    150    10641.498    0.9  -61918.0966  83201.0925  False
    75    160  -40639.0405 0.3293  -92212.9555  10934.8745  False
    75    180  -76345.8647 0.0211 -147350.1379  -5341.5915   True
    75    190  -49969.6742 0.1395  -105705.162   5765.8136  False
    80     85  -23473.1222 0.8357  -64900.7996  17954.5553  False
    80     90  -32867.9451 0.0263  -63973.9108  -1761.9793   True
    80    120   30425.4939 0.0371     803.1635  60047.8244   True
    80    150   20732.3077    0.9  -39658.3909  81123.0063  False
    80    160  -30548.2308 0.0875   -62828.417   1731.9554  False
    80    180  -66255.0549 0.0103 -124767.8615  -7742.2484   True
    80    190  -39878.8645 0.0344  -78461.0665  -1296.6624   True
    85     90   -9394.8229    0.9  -49914.2397   31124.594  False
    85    120   53898.6161  0.001   14506.6817  93290.5505   True
    85    150   44205.4299  0.601  -21531.0023  109941.862  False
    85    160   -7075.1086    0.9   -48502.786  34352.5688  False
    85    180  -42781.9328 0.6102 -106797.4822  21233.6166  False
    85    190  -16405.7423    0.9  -62911.9607  30100.4761  False
    90    120    63293.439  0.001   34955.2467  91631.6313   True
    90    150   53600.2527 0.1392   -6171.0377 113371.5432  False
    90    160    2319.7143    0.9  -28786.2515    33425.68  False
    90    180  -33387.1099 0.8128   -91260.413  24486.1932  False
    90    190   -7010.9194    0.9  -44616.1983  30594.3595  False
   120    150   -9693.1862    0.9  -68705.9689  49319.5964  False
   120    160  -60973.7247  0.001  -90596.0551 -31351.3943   True
   120    180  -96680.5489  0.001 -153770.1326 -39590.9652   True
   120    190  -70304.3584  0.001  -106691.971 -33916.7457   True
   150    160  -51280.5385 0.2095  -111671.237   9110.1601  False
   150    180  -86987.3626  0.012 -164632.5465  -9342.1788   True
   150    190  -60611.1722 0.0871 -124592.5161   3370.1717  False
   160    180  -35706.8242 0.7389  -94219.6307  22805.9823  False
   160    190   -9330.6337    0.9  -47912.8358  29251.5684  False
   180    190   26376.1905    0.9   -35835.741   88588.122  False
-----------------------------------------------------------------


data['Dwell_Type'].replace(40, 20, inplace = True)
data['Dwell_Type'].replace(70, 20, inplace = True)
data['Dwell_Type'].replace(75, 20, inplace = True)
data['Dwell_Type'].replace(80, 20, inplace = True)
data['Dwell_Type'].replace(150, 20, inplace = True)
data['Dwell_Type'].replace(45, 30, inplace = True)
data['Dwell_Type'].replace(180, 30, inplace = True)
data['Dwell_Type'].replace(190, 30, inplace = True)
data['Dwell_Type'].replace(85, 50, inplace = True)
data['Dwell_Type'].replace(90, 50, inplace = True)
data['Dwell_Type'].replace(160, 50, inplace = True)


data["Dwell_Type"].value_counts()

20     917
60     431
50     408
30     203
120    114
Name: Dwell_Type, dtype: int64


data["Dwell_Type"].value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ Dwell_Type', data = data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq          F        PR(>F)
Dwell_Type     1.0  2.588866e+11  2.588866e+11  59.243897  2.140789e-14
Residual    2071.0  9.049948e+12  4.369844e+09        NaN           NaN


tukey_Dwell_Type = pairwise_tukeyhsd(data.Property_Sale_Price, data.Dwell_Type, alpha = 0.05)
print(tukey_Dwell_Type)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
    20     30 -55371.9353  0.001 -67812.1704 -42931.7002   True
    20     50 -34845.8199  0.001   -44390.15 -25301.4897   True
    20     60  48233.2233  0.001  38866.7973  57599.6493   True
    20    120   21821.382 0.0018   5893.9961  37748.7679   True
    30     50  20526.1155  0.001   6751.0152  34301.2157   True
    30     60 103605.1586  0.001  89952.7198 117257.5974   True
    30    120  77193.3173  0.001  58422.5749  95964.0598   True
    50     60  83079.0431  0.001  72000.9624  94157.1239   True
    50    120  56667.2019  0.001  39676.7469  73657.6568   True
    60    120 -26411.8413  0.001 -43303.0004  -9520.6821   True
---------------------------------------------------------------


sns.countplot(data.Dwell_Type)
plt.title('Count plot of Dwell_Type')

Text(0.5, 1.0, 'Count plot of Dwell_Type')


sns.catplot(x = data.Dwell_Type , y = data.Property_Sale_Price, data = data)
plt.title('Cat plot of Dwell_Type vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of Dwell_Type vs Property_Sale_Price')


sns.boxplot(x = data.Dwell_Type , y = data.Property_Sale_Price, data = data)
plt.title('Boxplot of Dwell_Type vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot of Dwell_Type vs Property_Sale_Price')


data.Zone_Class.isnull().sum()

0


data.Zone_Class.value_counts()

RL         1611
RM          324
FV          102
RH           22
C (all)      14
Name: Zone_Class, dtype: int64


data.Zone_Class.value_counts().sum()

2073


sns.countplot(data.Zone_Class)
plt.title('Count plot of Zone_Class')

Text(0.5, 1.0, 'Count plot of Zone_Class')


data.groupby("Zone_Class")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Zone_Class', data = data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq          F        PR(>F)
Zone_Class     4.0  1.222868e+12  3.057171e+11  78.187693  7.820516e-62
Residual    2068.0  8.085966e+12  3.910041e+09        NaN           NaN


t_Zone_Class = pairwise_tukeyhsd(data.Property_Sale_Price, data.Zone_Class)
print(t_Zone_Class)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
 group1 group2   meandiff  p-adj     lower        upper    reject
-----------------------------------------------------------------
C (all)     FV  144163.937  0.001   95506.9159 192820.9581   True
C (all)     RH  64583.8701 0.0215    6218.3288 122949.4115   True
C (all)     RL 116363.7592  0.001   70539.4929 162188.0256   True
C (all)     RM  59167.2324 0.0049    12565.458 105769.0068   True
     FV     RH -79580.0668  0.001 -119711.0724 -39449.0613   True
     FV     RL -27800.1777  0.001  -45230.7422 -10369.6133   True
     FV     RM -84996.7046  0.001 -104379.3404 -65614.0689   True
     RH     RL  51779.8891 0.0011   15134.8991  88424.8791   True
     RH     RM  -5416.6378    0.9  -43029.3654  32196.0899  False
     RL     RM -57196.5269  0.001  -67590.9606 -46802.0931   True
-----------------------------------------------------------------


data['Zone_Class'].replace('RH', 'RM', inplace = True)


data.Zone_Class.value_counts()

RL         1611
RM          346
FV          102
C (all)      14
Name: Zone_Class, dtype: int64


data.Zone_Class.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ Zone_Class', data = data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F        PR(>F)
Zone_Class     3.0  1.222264e+12  4.074213e+11  104.241323  7.595204e-63
Residual    2069.0  8.086570e+12  3.908444e+09         NaN           NaN


t_Zone_Class = pairwise_tukeyhsd(data.Property_Sale_Price, data.Zone_Class)
print(t_Zone_Class)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
 group1 group2   meandiff  p-adj     lower        upper    reject
-----------------------------------------------------------------
C (all)     FV  144163.937  0.001   98350.8216 189977.0523   True
C (all)     RL 116363.7592  0.001   73217.8298 159509.6887   True
C (all)     RM  59511.6429 0.0028   15691.4668 103331.8189   True
     FV     RL -27800.1777  0.001  -44211.9605  -11388.395   True
     FV     RM -84652.2941  0.001 -102762.6009 -66541.9874   True
     RL     RM -56852.1164  0.001  -66376.4484 -47327.7843   True
-----------------------------------------------------------------


sns.countplot(data.Zone_Class)
plt.title('Count plot of Zone_Class')

Text(0.5, 1.0, 'Count plot of Zone_Class')


sns.catplot('Zone_Class', 'Property_Sale_Price', data = data)
plt.title('Cat plot of Zone_Class vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of Zone_Class vs Property_Sale_Price')


sns.boxplot('Zone_Class', 'Property_Sale_Price', data = data)
plt.title('Boxplot of Zone_Class vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot of Zone_Class vs Property_Sale_Price')


data.LotFrontage.isnull().sum()

320


data.LotFrontage.dtype

dtype('float64')


data.LotFrontage.sample(10)

951     60.0
1259    65.0
424     72.0
1690    52.0
1146     NaN
1904    78.0
1573    77.0
643     80.0
2065    73.0
1805    76.0
Name: LotFrontage, dtype: float64


data['LotFrontage'] = data["LotFrontage"].fillna(data['LotFrontage'].median())


data.LotFrontage.isnull().sum()

0


data.LotFrontage.describe()

count    2073.000000
mean       71.739026
std        25.746423
min        21.000000
25%        60.000000
50%        70.000000
75%        80.000000
max       313.000000
Name: LotFrontage, dtype: float64


data.LotFrontage.value_counts()

70.0     411
60.0     181
80.0      89
75.0      73
50.0      72
        ... 
138.0      1
137.0      1
150.0      1
112.0      1
46.0       1
Name: LotFrontage, Length: 110, dtype: int64


data.LotFrontage.value_counts().sum()

2073


plt.hist(data.LotFrontage)
plt.title('Hist of LotFrontage')

Text(0.5, 1.0, 'Hist of LotFrontage')


sns.distplot(data.LotFrontage)
plt.title('Normality of LotFrontage')

Text(0.5, 1.0, 'Normality of LotFrontage')


sns.countplot(data.LotFrontage)
plt.title('Count plot of LotFrontage')

Text(0.5, 1.0, 'Count plot of LotFrontage')


sns.boxplot(x = data.LotFrontage, data =data, color = 'r')
plt.title('Box plot of LotFrontage')

Text(0.5, 1.0, 'Box plot of LotFrontage')


IQR = data.LotFrontage.quantile(.75) - data.LotFrontage.quantile(.25)
IQR

20.0


upper_limit = data.LotFrontage.quantile(.75) + (1.5*IQR)
upper_limit

110.0


lower_limit = data.LotFrontage.quantile(.25) - (1.5*IQR)
lower_limit

30.0


len(data.LotFrontage[data.LotFrontage > upper_limit])

68


len(data.LotFrontage[data.LotFrontage < lower_limit])

52


for i in np.arange(upper_limit,200,40):
    outliers = len(data.LotFrontage[data.LotFrontage > i])
    print('At a limit of :', i, 'There are', outliers, 'outliers')

At a limit of : 110.0 There are 68 outliers
At a limit of : 150.0 There are 16 outliers
At a limit of : 190.0 There are 9 outliers


for i in np.arange(lower_limit,10,-10):
    outliers = len(data.LotFrontage[data.LotFrontage < i])
    print('At a limit of :', i, 'There are', outliers, 'outliers')

At a limit of : 30.0 There are 52 outliers
At a limit of : 20.0 There are 0 outliers


data['LotFrontage'] = np.where(data['LotFrontage'] > upper_limit, upper_limit, data['LotFrontage'])


data['LotFrontage'] = np.where(data['LotFrontage'] < lower_limit, lower_limit, data['LotFrontage'])


sns.boxplot(x = data.LotFrontage, data =data, color = 'g')
plt.title('Box plot of LotFrontage')

Text(0.5, 1.0, 'Box plot of LotFrontage')


sns.distplot(data.LotFrontage)
plt.title('Dist plot of LotFrontage')

Text(0.5, 1.0, 'Dist plot of LotFrontage')


np.corrcoef(data.LotFrontage, data.Property_Sale_Price)

array([[1.        , 0.32062379],
       [0.32062379, 1.        ]])


sns.scatterplot(x = data['LotFrontage'], y = data.Property_Sale_Price, data = data )
plt.xlabel('LotFrontage')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of LotFrontage vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter plot of LotFrontage vs Property_Sale_Price')


data.LotArea.isnull().sum()

0


data.LotArea.describe()

count      2073.000000
mean      10717.853353
std        9215.982306
min        1300.000000
25%        7620.000000
50%        9492.000000
75%       11601.000000
max      215245.000000
Name: LotArea, dtype: float64


data.LotArea.value_counts()

7200     28
9600     25
9000     20
6000     19
8400     15
         ..
7685      1
11777     1
17920     1
14720     1
8190      1
Name: LotArea, Length: 1367, dtype: int64


data.LotArea.value_counts().sum()

2073


plt.hist(data.LotArea)
plt.title('Hist of LotArea')

Text(0.5, 1.0, 'Hist of LotArea')


sns.distplot(data.LotArea)
plt.title('Normality of LotArea')

Text(0.5, 1.0, 'Normality of LotArea')


sns.countplot(data.LotArea)
plt.title('Count plot of LotArea')

Text(0.5, 1.0, 'Count plot of LotArea')


sns.boxplot(x = data.LotArea, data =data, color = 'r')
plt.title('Box plot of LotArea')

Text(0.5, 1.0, 'Box plot of LotArea')


IQR = data.LotArea.quantile(.75) - data.LotArea.quantile(.25)
IQR

3981.0


upper_limit = data.LotArea.quantile(.75) + (1.5*IQR)
upper_limit

17572.5


lower_limit = data.LotArea.quantile(.25) - (1.5*IQR)
lower_limit

1648.5


len(data.LotArea[data.LotArea > upper_limit])

127


len(data.LotArea[data.LotArea < lower_limit])

9


for i in np.arange(upper_limit,50000,10000):
    outliers = len(data.LotArea[data.LotArea > i])
    print('At a limit of :', i, 'There are', outliers, 'outliers')

At a limit of : 17572.5 There are 127 outliers
At a limit of : 27572.5 There are 56 outliers
At a limit of : 37572.5 There are 30 outliers
At a limit of : 47572.5 There are 11 outliers


for i in np.arange(lower_limit,1300,-1000):
    outliers = len(data.LotArea[data.LotArea < i])
    print('At a limit of :', i, 'There are', outliers, 'outliers')

At a limit of : 1648.5 There are 9 outliers


data['LotArea'] = np.where(data['LotArea'] > upper_limit, upper_limit, data['LotArea'])


data['LotArea'] = np.where(data['LotArea'] < lower_limit, lower_limit, data['LotArea'])


sns.boxplot(x = data.LotArea, data =data, color = 'g')
plt.title('Box plot of LotArea')

Text(0.5, 1.0, 'Box plot of LotArea')


sns.distplot(data.LotArea)
plt.title('Normality of LotArea')

Text(0.5, 1.0, 'Normality of LotArea')


np.corrcoef(data.LotArea, data.Property_Sale_Price)

array([[1.        , 0.35178959],
       [0.35178959, 1.        ]])


sns.scatterplot(x = data['LotArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('LotArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of LotArea vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter plot of LotArea vs Property_Sale_Price')


data.Road_Type.isnull().sum()

0


data.Road_Type.value_counts()

Pave    2063
Grvl      10
Name: Road_Type, dtype: int64


sns.countplot(data.Road_Type)
plt.title('Count plot of Road_Type')

Text(0.5, 1.0, 'Count plot of Road_Type')


data.groupby('Road_Type')['Property_Sale_Price'].describe()


df1 = data[data.Road_Type == 'Pave']

df2 = data[data.Road_Type == 'Grvl']

ttest_ind(df1.Property_Sale_Price, df2.Property_Sale_Price)

Ttest_indResult(statistic=2.765310980668061, pvalue=0.005737285634853254)


data.Alley.isnull().sum()

1944


data.Alley.value_counts()

Grvl    72
Pave    57
Name: Alley, dtype: int64


data['Alley'] = data['Alley'].fillna('No alley access')


data.Alley.isnull().sum()

0


data.Alley.value_counts()

No alley access    1944
Grvl                 72
Pave                 57
Name: Alley, dtype: int64


data.Alley.value_counts().sum()

2073


sns.countplot(data.Alley)
plt.title('Count plot of Alley')

Text(0.5, 1.0, 'Count plot of Alley')


data.groupby('Alley')['Property_Sale_Price'].describe()


mod = ols('Property_Sale_Price ~ Alley', data = data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq          F        PR(>F)
Alley        2.0  2.490114e+11  1.245057e+11  28.447221  6.490283e-13
Residual  2070.0  9.059823e+12  4.376726e+09        NaN           NaN


t_Alley = pairwise_tukeyhsd(data.Property_Sale_Price, data.Alley)
print(t_Alley)

             Multiple Comparison of Means - Tukey HSD, FWER=0.05              
==============================================================================
     group1          group2      meandiff  p-adj    lower      upper    reject
------------------------------------------------------------------------------
           Grvl No alley access 59844.1445  0.001 41222.1851  78466.104   True
           Grvl            Pave 55139.6418  0.001 27629.9535 82649.3301   True
No alley access            Pave -4704.5027 0.8409 -25555.771 16146.7656  False
------------------------------------------------------------------------------


data['Alley'].replace('Pave', 'No alley access', inplace = True)


data.Alley.value_counts()

No alley access    2001
Grvl                 72
Name: Alley, dtype: int64


data.Alley.value_counts().sum()

2073


df1=data[data.Alley=='No alley access']

df2=data[data.Alley=='Grvl']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=7.52556317937485, pvalue=7.781545291319476e-14)


sns.countplot(data.Alley)
plt.title('Count plot of Alley')

Text(0.5, 1.0, 'Count plot of Alley')


sns.catplot(x = 'Alley', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Alley vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Alley vs Property_Sale_Price')


sns.boxplot(x = 'Alley', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Alley vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Alley vs Property_Sale_Price')


data.Property_Shape.isnull().sum()

0


data.Property_Shape.value_counts()

Reg    1300
IR1     698
IR2      60
IR3      15
Name: Property_Shape, dtype: int64


data.Property_Shape.value_counts().sum()

2073


sns.countplot(data.Property_Shape)
plt.title('Count plot of Property_Shape')

Text(0.5, 1.0, 'Count plot of Property_Shape')


data.groupby("Property_Shape")["Property_Sale_Price"].describe()


mod= ols("Property_Sale_Price~Property_Shape",data =data).fit()
sm.stats.anova_lm(mod,type = 2)


t_Property_Shape = pairwise_tukeyhsd(data.Property_Sale_Price, data.Property_Shape, alpha = 0.05)
print(t_Property_Shape)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
   IR1    IR2  13181.6259 0.4252  -9068.3429  35431.5947  False
   IR1    IR3   2837.7259    0.9 -40321.0942   45996.546  False
   IR1    Reg -37951.9959  0.001 -45712.6173 -30191.3746   True
   IR2    IR3    -10343.9    0.9 -58086.6594  37398.8594  False
   IR2    Reg -51133.6218  0.001 -72971.9954 -29295.2482   True
   IR3    Reg -40789.7218 0.0698 -83737.7975   2158.3539  False
---------------------------------------------------------------


data["Property_Shape"].replace("IR3","IR1",inplace = True)


data.Property_Shape.value_counts()

Reg    1300
IR1     713
IR2      60
Name: Property_Shape, dtype: int64


data.Property_Shape.value_counts().sum()

2073


mod= ols("Property_Sale_Price~Property_Shape",data =data).fit()
sm.stats.anova_lm(mod,type = 2)


t_Property_Shape = pairwise_tukeyhsd(data.Property_Sale_Price, data.Property_Shape, alpha = 0.05)
print(t_Property_Shape)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
   IR1    IR2  13121.9262 0.2831  -7153.1446  33396.9969  False
   IR1    Reg -38011.6956  0.001 -45040.7753 -30982.6159   True
   IR2    Reg -51133.6218  0.001 -71050.2206  -31217.023   True
---------------------------------------------------------------


data["Property_Shape"].replace("IR2","IR1",inplace = True)


data.Property_Shape.value_counts()

Reg    1300
IR1     773
Name: Property_Shape, dtype: int64


data.Property_Shape.value_counts().sum()

2073


df1=data[data.Property_Shape=='Reg']

df2=data[data.Property_Shape=='IR1']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=-13.358349704280727, pvalue=4.188132301676014e-39)


sns.countplot(data.Property_Shape)
plt.title('Count plot of Property_Shape')

Text(0.5, 1.0, 'Count plot of Property_Shape')


sns.catplot(x = 'Property_Shape', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Property_Shape vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Property_Shape vs Property_Sale_Price')


sns.boxplot(x = 'Property_Shape', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Shape vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Property_Shape vs Property_Sale_Price')


data.LandContour.isnull().sum()

0


data.LandContour.value_counts()

Lvl    1854
Bnk      92
HLS      76
Low      51
Name: LandContour, dtype: int64


data.LandContour.value_counts().sum()

2073


sns.countplot(data.LandContour)
plt.title('Count plot of LandContour')

Text(0.5, 1.0, 'Count plot of LandContour')


data.groupby("LandContour")["Property_Sale_Price"].describe()


mod= ols("Property_Sale_Price~LandContour",data =data).fit()
aov_table_LandContour = sm.stats.anova_lm(mod)
print(aov_table_LandContour)

                 df        sum_sq       mean_sq          F        PR(>F)
LandContour     3.0  2.475121e+11  8.250404e+10  18.838405  4.702250e-12
Residual     2069.0  9.061322e+12  4.379566e+09        NaN           NaN


t_LandContour = pairwise_tukeyhsd(data.Property_Sale_Price, data.LandContour, alpha = 0.05)
print(t_LandContour)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
   Bnk    HLS  73508.5875  0.001  47133.5874  99883.5877   True
   Bnk    Low  56597.1149  0.001  26892.2517  86301.9781   True
   Bnk    Lvl  36109.1978  0.001   17934.752  54283.6437   True
   HLS    Low -16911.4727 0.4924 -47711.3166  13888.3713  False
   HLS    Lvl -37399.3897  0.001 -57313.2535 -17485.5259   True
   Low    Lvl  -20487.917 0.1286 -44639.5264   3663.6924  False
---------------------------------------------------------------


data["LandContour"].replace("Low","HLS",inplace = True)


data.LandContour.value_counts()

Lvl    1854
HLS     127
Bnk      92
Name: LandContour, dtype: int64


data.LandContour.value_counts().sum()

2073


mod= ols("Property_Sale_Price~LandContour",data =data).fit()
aov_table_LandContour = sm.stats.anova_lm(mod)
print(aov_table_LandContour)

                 df        sum_sq       mean_sq          F        PR(>F)
LandContour     2.0  2.387835e+11  1.193918e+11  27.248025  2.086517e-12
Residual     2070.0  9.070051e+12  4.381667e+09        NaN           NaN


t_LandContour = pairwise_tukeyhsd(data.Property_Sale_Price, data.LandContour, alpha = 0.05)
print(t_LandContour)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05     
=============================================================
group1 group2   meandiff  p-adj    lower      upper    reject
-------------------------------------------------------------
   Bnk    HLS  66717.3662 0.001  45462.1378 87972.5947   True
   Bnk    Lvl  36109.1978 0.001  19526.2348 52692.1609   True
   HLS    Lvl -30608.1684 0.001 -44848.6608 -16367.676   True
-------------------------------------------------------------


sns.countplot(data.LandContour)
plt.title('Count plot of LandContour')

Text(0.5, 1.0, 'Count plot of LandContour')


sns.catplot(x = 'LandContour', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Property_Sale_Price vs LandContour')

Text(0.5, 1.0, 'Cat Plot of Property_Sale_Price vs LandContour')


sns.boxplot(x = 'LandContour', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Sale_Price vs LandContour')

Text(0.5, 1.0, 'Box Plot of Property_Sale_Price vs LandContour')


data.Utilities.isnull().sum()

0


data.Utilities.value_counts()

AllPub    2070
NoSeWa       3
Name: Utilities, dtype: int64


data.Utilities.value_counts().sum()

2073


sns.countplot(data.Utilities)
plt.title('Count plot of Utilities')

Text(0.5, 1.0, 'Count plot of Utilities')


data.groupby("Utilities")["Property_Sale_Price"].describe()


df1=data[data.Utilities=='AllPub']

df2=data[data.Utilities=='NoSeWa']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=1.036797848433364, pvalue=0.29995111758233073)


data.LotConfig.isnull().sum()

0


data.LotConfig.value_counts()

Inside     1481
Corner      386
CulDSac     137
FR2          65
FR3           4
Name: LotConfig, dtype: int64


data.LotConfig.value_counts().sum()

2073


sns.countplot(data.LotConfig)
plt.title('Count plot of LotConfig')

Text(0.5, 1.0, 'Count plot of LotConfig')


data.groupby("LotConfig")["Property_Sale_Price"].describe()


mod = ols("Property_Sale_Price~LotConfig", data =data).fit()
aov_table_LotConfig = sm.stats.anova_lm(mod)
print(aov_table_LotConfig)

               df        sum_sq       mean_sq         F    PR(>F)
LotConfig     4.0  1.455458e+11  3.638646e+10  8.211811  0.000001
Residual   2068.0  9.163288e+12  4.430990e+09       NaN       NaN


t_LotConfig = pairwise_tukeyhsd(data.Property_Sale_Price, data.LotConfig, alpha = 0.05)
print(t_LotConfig)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05        
==================================================================
 group1  group2   meandiff  p-adj     lower        upper    reject
------------------------------------------------------------------
 Corner CulDSac  28775.5825  0.001   10702.2906  46848.8744   True
 Corner     FR2   2145.5545    0.9   -22220.112   26511.221  False
 Corner     FR3  29603.8161    0.9  -61733.6609  120941.293  False
 Corner  Inside  -4625.7241 0.7153  -15011.5571   5760.1088  False
CulDSac     FR2  -26630.028 0.0611  -54001.5582    741.5023  False
CulDSac     FR3    828.2336    0.9  -91356.6331  93013.1003  False
CulDSac  Inside -33401.3066  0.001  -49630.3081 -17172.3051   True
    FR2     FR3  27458.2615    0.9  -66163.8057 121080.3288  False
    FR2  Inside  -6771.2786    0.9  -29802.1777  16259.6204  False
    FR3  Inside -34229.5402 0.8224 -125220.0413   56760.961  False
------------------------------------------------------------------


data["LotConfig"].replace("FR2","Corner",inplace = True)
data["LotConfig"].replace("FR3","Corner",inplace = True)


data.LotConfig.value_counts()

Inside     1481
Corner      455
CulDSac     137
Name: LotConfig, dtype: int64


data.LotConfig.value_counts().sum()

2073


mod = ols("Property_Sale_Price~LotConfig", data =data).fit()
aov_table_LotConfig = sm.stats.anova_lm(mod)
print(aov_table_LotConfig)

               df        sum_sq       mean_sq          F        PR(>F)
LotConfig     2.0  1.418872e+11  7.094361e+10  16.019867  1.247252e-07
Residual   2070.0  9.166947e+12  4.428477e+09        NaN           NaN


t_LotConfig = pairwise_tukeyhsd(data.Property_Sale_Price, data.LotConfig, alpha = 0.05)
print(t_LotConfig)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
 group1  group2   meandiff  p-adj     lower       upper    reject
-----------------------------------------------------------------
 Corner CulDSac  28208.8214  0.001  12998.3781  43419.2646   True
 Corner  Inside  -5192.4852 0.3132  -13558.458   3173.4875  False
CulDSac  Inside -33401.3066  0.001 -47339.2419 -19463.3713   True
-----------------------------------------------------------------


data["LotConfig"].replace("CulDSac","Corner",inplace = True)


data.LotConfig.value_counts()

Inside    1481
Corner     592
Name: LotConfig, dtype: int64


data.LotConfig.value_counts().sum()

2073


df1=data[data.LotConfig=='Inside']

df2=data[data.LotConfig=='Corner']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=-3.6065196077561565, pvalue=0.00031767711757648353)


sns.countplot(data.LotConfig)
plt.title('Count plot of LotConfig')

Text(0.5, 1.0, 'Count plot of LotConfig')


sns.catplot(x = 'LotConfig', y = 'Property_Sale_Price', data = data)
plt.title('LotConfig vs Property_Sale_Price')

Text(0.5, 1.0, 'LotConfig vs Property_Sale_Price')


sns.boxplot(x = 'LotConfig', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of LotConfig vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of LotConfig vs Property_Sale_Price')


data.LandSlope.isnull().sum()

0


data.LandSlope.value_counts()

Gtl    1958
Mod     101
Sev      14
Name: LandSlope, dtype: int64


data.LandSlope.value_counts().sum()

2073


sns.countplot(data.LandSlope)
plt.title('Count Plot of LandSlope')

Text(0.5, 1.0, 'Count Plot of LandSlope')


data.groupby("LandSlope")["Property_Sale_Price"].describe()


mod = ols("Property_Sale_Price~LandSlope",data =data).fit()
aov_table_LandSlope = sm.stats.anova_lm(mod)
print(aov_table_LandSlope)

               df        sum_sq       mean_sq         F    PR(>F)
LandSlope     2.0  1.636691e+10  8.183453e+09  1.822955  0.161807
Residual   2070.0  9.292467e+12  4.489115e+09       NaN       NaN


data.Neighborhood.isnull().sum()

0


data.Neighborhood.value_counts()

NAmes      317
CollgCr    217
OldTown    156
Edwards    138
Somerst    123
Gilbert    119
Sawyer     108
NWAmes     105
NridgHt     99
SawyerW     93
BrkSide     80
Mitchel     78
Crawfor     72
NoRidge     65
Timber      53
IDOTRR      51
ClearCr     33
StoneBr     32
SWISU       31
Blmngtn     27
MeadowV     24
BrDale      17
NPkVill     17
Veenker     15
Blueste      3
Name: Neighborhood, dtype: int64


data.Neighborhood.value_counts().sum()

2073


sns.countplot(data.Neighborhood)
plt.title('Count plot of Neighborhood')

Text(0.5, 1.0, 'Count plot of Neighborhood')


data.groupby("Neighborhood")["Property_Sale_Price"].describe()


mod = ols("Property_Sale_Price~Neighborhood",data =data).fit()
aov_table_Neighborhood = sm.stats.anova_lm(mod)
print(aov_table_Neighborhood)

                  df        sum_sq       mean_sq           F  PR(>F)
Neighborhood    24.0  5.406891e+12  2.252871e+11  118.245708     0.0
Residual      2048.0  3.901943e+12  1.905246e+09         NaN     NaN


t_Neighborhood = pairwise_tukeyhsd(data.Property_Sale_Price, data.Neighborhood, alpha = 0.05)
print(t_Neighborhood)

        Multiple Comparison of Means - Tukey HSD, FWER=0.05         
====================================================================
 group1  group2   meandiff   p-adj     lower        upper     reject
--------------------------------------------------------------------
Blmngtn Blueste  -57835.6667 0.8804 -155142.6019   39471.2686  False
Blmngtn  BrDale  -85547.4314  0.001 -135052.0446  -36042.8181   True
Blmngtn BrkSide  -65700.9792  0.001 -101287.9227  -30114.0356   True
Blmngtn ClearCr   26016.8182 0.8004  -15474.9984   67508.6348  False
Blmngtn CollgCr    7866.9462    0.9  -24762.4352   40496.3276  False
Blmngtn Crawfor   19483.9306    0.9  -16598.4467   55566.3078  False
Blmngtn Edwards  -60709.6522  0.001  -94356.6425  -27062.6619   True
Blmngtn Gilbert     -14.3978    0.9  -34098.0983   34069.3028  False
Blmngtn  IDOTRR   -90894.451  0.001 -128948.9499  -52839.9521   True
Blmngtn MeadowV  -94473.1667  0.001 -139329.4474  -49616.8859   True
Blmngtn Mitchel  -35033.8205 0.0629  -70735.7052     668.0641  False
Blmngtn   NAmes  -46015.8149  0.001   -78070.638  -13960.9918   True
Blmngtn NPkVill  -46615.0784 0.0979  -96119.6917    2889.5348  False
Blmngtn  NWAmes     786.8095    0.9  -33714.5422   35288.1612  False
Blmngtn NoRidge  105981.7795  0.001   69373.3558  142590.2032   True
Blmngtn NridgHt   99133.7879  0.001   64419.2435  133848.3323   True
Blmngtn OldTown  -68338.4936  0.001 -101666.3238  -35010.6634   True
Blmngtn   SWISU  -47134.5699 0.0101  -89224.3778   -5044.7619   True
Blmngtn  Sawyer   -49262.787  0.001  -83665.9839  -14859.5902   True
Blmngtn SawyerW   -5656.5484    0.9  -40610.2314   29297.1346  False
Blmngtn Somerst   33747.7073 0.0544    -233.3485   67728.7631  False
Blmngtn StoneBr   79836.2083  0.001   38053.6709  121618.7458   True
Blmngtn  Timber   43871.7484 0.0055    6066.6082   81676.8886   True
Blmngtn Veenker   67264.3333  0.001    15774.343  118754.3236   True
Blueste  BrDale  -27711.7647    0.9  -127839.775   72416.2456  False
Blueste BrkSide   -7865.3125    0.9 -101893.7245   86163.0995  False
Blueste ClearCr   83852.4848  0.202   -12565.784  180270.7537  False
Blueste CollgCr   65702.6129 0.6049  -27246.7725  158651.9983  False
Blueste Crawfor   77319.5972 0.3071  -16897.4377  171536.6321  False
Blueste Edwards   -2873.9855    0.9  -96185.4621   90437.4911  False
Blueste Gilbert   57821.2689  0.821  -35648.5677  151291.1055  False
Blueste  IDOTRR  -33058.7843    0.9 -128048.5554   61930.9867  False
Blueste MeadowV     -36637.5    0.9 -134550.7148   61275.7148  False
Blueste Mitchel   22801.8462    0.9  -71270.1278  116873.8201  False
Blueste   NAmes   11819.8517    0.9  -80929.3984  104569.1019  False
Blueste NPkVill   11220.5882    0.9  -88907.4221  111348.5985  False
Blueste  NWAmes   58622.4762 0.8026  -35000.4643  152245.4167  False
Blueste NoRidge  163817.4462  0.001   69397.7003   258237.192   True
Blueste NridgHt  156969.4545  0.001     63267.74  250671.1691   True
Blueste OldTown  -10502.8269    0.9 -103699.6937   82694.0399  False
Blueste   SWISU   10701.0968    0.9  -85976.0135   107378.207  False
Blueste  Sawyer    8572.8796    0.9  -85013.9339  102159.6932  False
Blueste SawyerW   52179.1183    0.9  -41611.4552  145969.6917  False
Blueste Somerst    91583.374 0.0638   -1849.0822  185015.8302  False
Blueste StoneBr   137671.875  0.001   41128.1432  234215.6068   True
Blueste  Timber  101707.4151 0.0198    6817.2663  196597.5639   True
Blueste Veenker     125100.0 0.0016   23975.6666  226224.3334   True
 BrDale BrkSide   19846.4522    0.9  -22854.9454   62547.8498  False
 BrDale ClearCr  111564.2496  0.001   63830.0936  159298.4055   True
 BrDale CollgCr   93414.3776  0.001   53144.5957  133684.1595   True
 BrDale Crawfor  105031.3619  0.001    61916.205  148146.5188   True
 BrDale Edwards   24837.7792  0.856  -16260.8675   65936.4259  False
 BrDale Gilbert   85533.0336  0.001   44076.0988  126989.9685   True
 BrDale  IDOTRR   -5347.0196    0.9  -50125.6271   39431.5879  False
 BrDale MeadowV   -8925.7353    0.9  -59611.6774   41760.2068  False
 BrDale Mitchel   50513.6109 0.0041    7716.3753   93310.8464   True
 BrDale   NAmes   39531.6164 0.0545    -274.0425   79337.2754  False
 BrDale NPkVill   38932.3529 0.5974  -15910.0169   93774.7228  False
 BrDale  NWAmes   86334.2409  0.001   44533.2592  128135.2226   True
 BrDale NoRidge  191529.2109  0.001  147972.8624  235085.5593   True
 BrDale NridgHt  184681.2193  0.001  142704.1018  226658.3368   True
 BrDale OldTown   17208.9378    0.9  -23628.8274    58046.703  False
 BrDale   SWISU   38412.8615 0.3722   -9841.9905   86667.7134  False
 BrDale  Sawyer   36284.6443 0.2019     -5435.36   78004.6486  False
 BrDale SawyerW    79890.883  0.001   37715.7866  122065.9794   True
 BrDale Somerst  119295.1387  0.001   77922.5517  160667.7257   True
 BrDale StoneBr  165383.6397  0.001  117396.5661  213370.7134   True
 BrDale  Timber  129419.1798  0.001   84852.2927  173986.0669   True
 BrDale Veenker  152811.7647  0.001   96170.8086  209452.7208   True
BrkSide ClearCr   91717.7973  0.001   58637.9928  124797.6019   True
BrkSide CollgCr   73567.9254  0.001   52654.3137   94481.5371   True
BrkSide Crawfor   85184.9097  0.001   59211.0657  111158.7538   True
BrkSide Edwards     4991.327    0.9  -17476.9302   27459.5842  False
BrkSide Gilbert   65686.5814  0.001   42569.4607   88803.7021   True
BrkSide  IDOTRR  -25193.4718 0.1843  -53843.9083    3456.9647  False
BrkSide MeadowV  -28772.1875 0.4377  -65984.9346    8440.5596  False
BrkSide Mitchel   30667.1587 0.0027    5224.5332   56109.7841   True
BrkSide   NAmes   19685.1642 0.0609    -320.1927   39690.5212  False
BrkSide NPkVill   19085.9007    0.9  -23615.4968   61787.2983  False
BrkSide  NWAmes   66487.7887  0.001   42759.2006   90216.3768   True
BrkSide NoRidge  171682.7587  0.001  144982.9588  198382.5585   True
BrkSide NridgHt   164834.767  0.001  140797.2502  188872.2839   True
BrkSide OldTown   -2637.5144    0.9  -24624.9401   19349.9113  False
BrkSide   SWISU   18566.4093    0.9  -15260.4226   52393.2412  False
BrkSide  Sawyer   16438.1921 0.6291   -7147.4514   40023.8356  False
BrkSide SawyerW   60044.4308  0.001   35662.8275    84426.034   True
BrkSide Somerst   99448.6865  0.001   76483.1737  122414.1992   True
BrkSide StoneBr  145537.1875  0.001  112093.4578  178980.9172   True
BrkSide  Timber  109572.7276  0.001   81254.3369  137891.1182   True
BrkSide Veenker  132965.3125  0.001   87977.2949  177953.3301   True
ClearCr CollgCr  -18149.8719 0.8482  -48024.9359    11725.192  False
ClearCr Crawfor   -6532.8876    0.9  -40145.1007   27079.3254  False
ClearCr Edwards  -86726.4704  0.001  -117709.738  -55743.2027   True
ClearCr Gilbert  -26031.2159 0.2909  -57488.1955    5425.7636  False
ClearCr  IDOTRR -116911.2692  0.001 -152632.2406  -81190.2977   True
ClearCr MeadowV -120489.9848  0.001 -163384.3766  -77595.5931   True
ClearCr Mitchel  -61050.6387  0.001  -94254.0645  -27847.2129   True
ClearCr   NAmes  -72032.6331  0.001  -101279.079  -42786.1872   True
ClearCr NPkVill  -72631.8966  0.001 -120366.0526  -24897.7406   True
ClearCr  NWAmes  -25230.0087 0.3875  -57139.0385    6679.0212  False
ClearCr NoRidge   79964.9613  0.001   45788.6595  114141.2631   True
ClearCr NridgHt   73116.9697  0.001   40977.5468  105256.3926   True
ClearCr OldTown  -94355.3118  0.001  -124991.682  -63718.9415   True
ClearCr   SWISU  -73151.3881  0.001  -113143.823  -33158.9532   True
ClearCr  Sawyer  -75279.6052  0.001 -107082.4804    -43476.73   True
ClearCr SawyerW  -31673.3666 0.0658  -64070.9416     724.2085  False
ClearCr Somerst    7730.8891    0.9  -23614.8453   39076.6236  False
ClearCr StoneBr   53819.3902  0.001   14150.4684   93488.3119   True
ClearCr  Timber   17854.9302    0.9  -17600.2745    53310.135  False
ClearCr Veenker   41247.5152 0.2888   -8542.6648   91037.6951  False
CollgCr Crawfor   11616.9843    0.9  -10128.9706   33362.9392  False
CollgCr Edwards  -68576.5984  0.001  -85985.4628   -51167.734   True
CollgCr Gilbert    -7881.344    0.9  -26119.9637   10357.2757  False
CollgCr  IDOTRR  -98761.3972  0.001 -123642.9866  -73879.8079   True
CollgCr MeadowV -102340.1129  0.001 -136735.3819  -67944.8439   True
CollgCr Mitchel  -42900.7667  0.001  -64009.3709  -21792.1626   True
CollgCr   NAmes  -53882.7612  0.001  -67970.3545  -39795.1678   True
CollgCr NPkVill  -54482.0247  0.001  -94751.8066  -14212.2428   True
CollgCr  NWAmes   -7080.1367    0.9   -26087.816   11927.5426  False
CollgCr NoRidge   98114.8333  0.001    75506.753  120722.9135   True
CollgCr NridgHt   91266.8416  0.001   71874.8796  110658.8037   True
CollgCr OldTown  -76205.4398  0.001  -92989.1483  -59421.7314   True
CollgCr   SWISU  -55001.5161  0.001  -85701.6877  -24301.3445   True
CollgCr  Sawyer  -57129.7333  0.001   -75958.662  -38300.8045   True
CollgCr SawyerW  -13523.4946 0.6647  -33340.3702    6293.3809  False
CollgCr Somerst   25880.7611  0.001     7834.688   43926.8342   True
CollgCr StoneBr   71969.2621  0.001   41691.7286  102246.7956   True
CollgCr  Timber   36004.8022  0.001   11506.2874    60503.317   True
CollgCr Veenker   59397.3871  0.001   16710.5308  102084.2434   True
Crawfor Edwards  -80193.5827  0.001 -103438.5818  -56948.5837   True
Crawfor Gilbert  -19498.3283 0.3164  -43371.0883    4374.4316  False
Crawfor  IDOTRR -110378.3815  0.001 -139641.9239  -81114.8392   True
Crawfor MeadowV -113957.0972  0.001 -151643.9112  -76270.2833   True
Crawfor Mitchel  -54517.7511  0.001  -80648.8549  -28386.6472   True
Crawfor   NAmes  -65499.7455  0.001  -86373.6935  -44625.7975   True
Crawfor NPkVill   -66099.009  0.001 -109214.1659  -22983.8521   True
Crawfor  NWAmes   -18697.121 0.4624  -43162.4698    5768.2278  False
Crawfor NoRidge   86497.8489  0.001   59141.1914  113854.5064   True
Crawfor NridgHt   79649.8573  0.001   54884.7687  104414.9459   True
Crawfor OldTown  -87822.4241  0.001 -110602.9924  -65041.8559   True
Crawfor   SWISU  -66618.5004  0.001 -100966.1635  -32270.8374   True
Crawfor  Sawyer  -68746.7176  0.001  -93073.4514  -44419.9838   True
Crawfor SawyerW  -25140.4789  0.049  -50239.6816     -41.2762   True
Crawfor Somerst   14263.7768 0.8637   -9462.2042   37989.7578  False
Crawfor StoneBr   60352.2778  0.001   26381.8427   94322.7128   True
Crawfor  Timber   24387.8179 0.2561   -4550.7144   53326.3501  False
Crawfor Veenker   47780.4028 0.0255    2399.4693   93161.3362   True
Edwards Gilbert   60695.2544  0.001   40692.9877   80697.5211   True
Edwards  IDOTRR  -30184.7988 0.0063  -56386.6483   -3982.9493   True
Edwards MeadowV  -33763.5145 0.0855  -69125.6122    1598.5832  False
Edwards Mitchel   25675.8317 0.0083     3025.962   48325.7013   True
Edwards   NAmes   14693.8372 0.1496    -1612.717   31000.3915  False
Edwards NPkVill   14094.5737    0.9   -27004.073   55193.2204  False
Edwards  NWAmes   61496.4617  0.001   40790.5375   82202.3859   True
Edwards NoRidge  166691.4317  0.001  142637.9763  190744.8871   True
Edwards NridgHt  159843.4401  0.001  138784.1993  180902.6808   True
Edwards OldTown   -7628.8414    0.9  -26314.0306   11056.3478  False
Edwards   SWISU   13575.0823    0.9  -18204.5332   45354.6978  False
Edwards  Sawyer   11446.8651    0.9   -9095.0915   31988.8218  False
Edwards SawyerW   55053.1038  0.001   33601.9504   76504.2572   True
Edwards Somerst   94457.3595  0.001   74630.5045  114284.2145   True
Edwards StoneBr  140545.8605  0.001  109174.3374  171917.3836   True
Edwards  Timber  104581.4006  0.001   78743.0443  130419.7569   True
Edwards Veenker  127973.9855  0.001   84504.3279  171443.6431   True
Gilbert  IDOTRR  -90880.0532  0.001 -117640.3896  -64119.7168   True
Gilbert MeadowV  -94458.7689  0.001 -130236.6481  -58680.8897   True
Gilbert Mitchel  -35019.4228  0.001  -58313.0975   -11725.748   True
Gilbert   NAmes  -46001.4172  0.001  -63191.0187  -28811.8157   True
Gilbert NPkVill  -46600.6807 0.0095  -88057.6155   -5143.7458   True
Gilbert  NWAmes     801.2073    0.9  -20607.0618   22209.4764  False
Gilbert NoRidge  105996.1772  0.001   81335.5324  130656.8221   True
Gilbert NridgHt   99148.1856  0.001   77398.0062  120898.3651   True
Gilbert OldTown  -68324.0958  0.001  -87784.6966  -48863.4951   True
Gilbert   SWISU  -47120.1721  0.001  -79361.8013   -14878.543   True
Gilbert  Sawyer  -49248.3893  0.001   -70498.111  -27998.6676   True
Gilbert SawyerW   -5642.1506    0.9  -27772.0099   16487.7086  False
Gilbert Somerst   33762.1051  0.001   13202.8525   54321.3577   True
Gilbert StoneBr   79850.6061  0.001   48011.1471  111690.0651   True
Gilbert  Timber   43886.1462  0.001   17481.6136   70290.6788   True
Gilbert Veenker   67278.7311  0.001   23470.1724  111087.2898   True
 IDOTRR MeadowV   -3578.7157    0.9   -43157.787   36000.3556  False
 IDOTRR Mitchel   55860.6305  0.001   27067.5493   84653.7117   True
 IDOTRR   NAmes    44878.636  0.001   20755.4389   69001.8332   True
 IDOTRR NPkVill   44279.3725 0.0574    -499.2349     89057.98  False
 IDOTRR  NWAmes   91681.2605  0.001    64390.965   118971.556   True
 IDOTRR NoRidge  196876.2305  0.001  166966.4721  226785.9888   True
 IDOTRR NridgHt  190028.2389  0.001  162468.9111  217587.5667   True
 IDOTRR OldTown   22555.9574 0.1929   -3234.7623   48346.6771  False
 IDOTRR   SWISU   43759.8811 0.0029    7346.0253   80173.7368   True
 IDOTRR  Sawyer   41631.6639  0.001   14465.5653   68797.7625   True
 IDOTRR SawyerW   85237.9026  0.001   57377.9508  113097.8544   True
 IDOTRR Somerst  124642.1583  0.001   98012.6801  151271.6365   True
 IDOTRR StoneBr  170730.6593  0.001  134672.4093  206788.9093   True
 IDOTRR  Timber  134766.1994  0.001  103403.0748   166129.324   True
 IDOTRR Veenker  158158.7843  0.001  111194.5846  205122.9841   True
MeadowV Mitchel   59439.3462  0.001   22116.6645   96762.0278   True
MeadowV   NAmes   48457.3517  0.001   14606.6549   82308.0486   True
MeadowV NPkVill   47858.0882 0.0951   -2827.8539   98544.0303  False
MeadowV  NWAmes   95259.9762  0.001   59083.9998  131435.9525   True
MeadowV NoRidge  200454.9462  0.001  162264.1792  238645.7131   True
MeadowV NridgHt  193606.9545  0.001  157227.5979  229986.3111   True
MeadowV OldTown   26134.6731 0.5103   -8923.8817   61193.2278  False
MeadowV   SWISU   47338.5968 0.0157    3865.5025   90811.6911   True
MeadowV  Sawyer   45210.3796 0.0013    9128.0023   81292.7569   True
MeadowV SawyerW   88816.6183  0.001   52208.9968  125424.2397   True
MeadowV Somerst   128220.874  0.001   92540.7654  163900.9825   True
MeadowV StoneBr   174309.375  0.001  131133.7056  217485.0444   True
MeadowV  Timber  138344.9151  0.001   99005.5376  177684.2926   True
MeadowV Veenker     161737.5  0.001  109110.7283  214364.2717   True
Mitchel   NAmes  -10981.9944    0.9  -31191.1093    9227.1204  False
Mitchel NPkVill  -11581.2579    0.9  -54378.4935   31215.9776  False
Mitchel  NWAmes     35820.63  0.001   11920.0044   59721.2557   True
Mitchel NoRidge     141015.6  0.001  114162.7915  167868.4085   True
Mitchel NridgHt  134167.6084  0.001  109960.2494  158374.9674   True
Mitchel OldTown  -33304.6731  0.001    -55477.65  -11131.6962   True
Mitchel   SWISU  -12100.7494    0.9  -46048.4823   21846.9836  False
Mitchel  Sawyer  -14228.9665 0.8693  -37987.6827    9529.7496  False
Mitchel SawyerW   29377.2721 0.0031    4828.2071   53926.3371   True
Mitchel Somerst   68781.5278  0.001   45638.3044   91924.7513   True
Mitchel StoneBr  114870.0288  0.001   81304.0182  148436.0395   True
Mitchel  Timber   78905.5689  0.001   50442.8694  107368.2685   True
Mitchel Veenker  102298.1538  0.001   57219.1594  147377.1483   True
  NAmes NPkVill    -599.2635    0.9  -40404.9225   39206.3955  False
  NAmes  NWAmes   46802.6245  0.001   28799.0963   64806.1526   True
  NAmes NoRidge  151997.5944  0.001  130226.9618   173768.227   True
  NAmes NridgHt  145149.6028  0.001  126740.8183  163558.3873   True
  NAmes OldTown  -22322.6787  0.001  -37960.0704   -6685.2869   True
  NAmes   SWISU    -1118.755    0.9  -31207.5517   28970.0418  False
  NAmes  Sawyer   -3246.9721    0.9   -21061.677   14567.7328  False
  NAmes SawyerW   40359.2665  0.001   21503.3998   59215.1332   True
  NAmes Somerst   79763.5222  0.001    62778.355   96748.6895   True
  NAmes StoneBr  125852.0233  0.001   96194.5758  155509.4707   True
  NAmes  Timber   89887.5634  0.001   66159.6814  113615.4453   True
  NAmes Veenker  113280.1483  0.001   71030.8541  155529.4424   True
NPkVill  NWAmes    47401.888 0.0082    5600.9062   89202.8697   True
NPkVill NoRidge  152596.8579  0.001  109040.5095  196153.2064   True
NPkVill NridgHt  145748.8663  0.001  103771.7488  187725.9838   True
NPkVill OldTown  -21723.4152    0.9  -62561.1804   19114.3501  False
NPkVill   SWISU    -519.4915    0.9  -48774.3434   47735.3605  False
NPkVill  Sawyer   -2647.7086    0.9  -44367.7129   39072.2957  False
NPkVill SawyerW     40958.53  0.071   -1216.5664   83133.6264  False
NPkVill Somerst   80362.7857  0.001   38990.1988  121735.3727   True
NPkVill StoneBr  126451.2868  0.001   78464.2131  174438.3604   True
NPkVill  Timber   90486.8269  0.001   45919.9397   135053.714   True
NPkVill Veenker  113879.4118  0.001   57238.4557  170520.3678   True
 NWAmes NoRidge    105194.97  0.001   79960.2314  130429.7085   True
 NWAmes NridgHt   98346.9784  0.001   75947.9844  120745.9723   True
 NWAmes OldTown  -69125.3031  0.001  -89308.4547  -48942.1516   True
 NWAmes   SWISU  -47921.3794  0.001  -80604.2078   -15238.551   True
 NWAmes  Sawyer  -50049.5966  0.001  -71962.9554  -28136.2377   True
 NWAmes SawyerW   -6443.3579    0.9  -29211.2145   16324.4987  False
 NWAmes Somerst   32960.8978  0.001   11716.4281   54205.3675   True
 NWAmes StoneBr   79049.3988  0.001   46763.2443  111335.5534   True
 NWAmes  Timber   43084.9389  0.001   16143.4474   70026.4304   True
 NWAmes Veenker   66477.5238  0.001   22343.2464  110611.8012   True
NoRidge NridgHt   -6847.9916    0.9  -32373.4368   18677.4535  False
NoRidge OldTown -174320.2731  0.001 -197925.2095 -150715.3366   True
NoRidge   SWISU -153116.3494  0.001  -188016.216 -118216.4827   True
NoRidge  Sawyer -155244.5665  0.001 -180344.9393 -130144.1937   True
NoRidge SawyerW -111638.3279  0.001 -137488.0612  -85788.5945   True
NoRidge Somerst  -72234.0722  0.001  -96752.6551  -47715.4892   True
NoRidge StoneBr  -26145.5712  0.481  -60674.2432    8383.1009  False
NoRidge  Timber  -62110.0311  0.001  -91701.8777  -32518.1845   True
NoRidge Veenker  -38717.4462 0.2504  -84517.7503     7082.858  False
NridgHt OldTown -167472.2815  0.001 -188017.7415 -146926.8214   True
NridgHt   SWISU -146268.3578  0.001 -179176.1623 -113360.5533   True
NridgHt  Sawyer -148396.5749  0.001  -170644.083 -126149.0668   True
NridgHt SawyerW -104790.3363  0.001 -127879.9793  -81700.6932   True
NridgHt Somerst  -65386.0806  0.001  -86975.0549  -43797.1062   True
NridgHt StoneBr  -19297.5795 0.8825   -51811.455   13216.2959  False
NridgHt  Timber  -55262.0395  0.001  -82476.0117  -28048.0672   True
NridgHt Veenker  -31869.4545 0.5742  -76170.5918   12431.6827  False
OldTown   SWISU   21203.9237 0.6844  -10237.5818   52645.4292  False
OldTown  Sawyer   19075.7066 0.0871    -939.1952   39090.6083  False
OldTown SawyerW   62681.9452  0.001   41734.9576   83628.9328   True
OldTown Somerst  102086.2009  0.001   82805.9393  121366.4625   True
OldTown StoneBr  148174.7019  0.001  117145.7353  179203.6686   True
OldTown  Timber   112210.242  0.001   86788.8935  137631.5906   True
OldTown Veenker  135602.8269  0.001   92379.7378  178825.9161   True
  SWISU  Sawyer   -2128.2171    0.9  -34707.4123   30450.9781  False
  SWISU SawyerW   41478.0215 0.0013    8318.0463   74637.9968   True
  SWISU Somerst   80882.2772  0.001   48749.1766  113015.3778   True
  SWISU StoneBr  126970.7782  0.001    86676.803  167264.7534   True
  SWISU  Timber   91006.3183  0.001   54853.1355  127159.5012   True
  SWISU Veenker  114398.9032  0.001   64109.3108  164688.4956   True
 Sawyer SawyerW   43606.2386  0.001   20987.3974   66225.0799   True
 Sawyer Somerst   83010.4944  0.001   61925.8037   104095.185   True
 Sawyer StoneBr  129098.9954  0.001   96917.7514  161280.2393   True
 Sawyer  Timber   93134.5355  0.001   66318.8562  119950.2147   True
 Sawyer Veenker  116527.1204  0.001   72469.5316  160584.7091   True
SawyerW Somerst   39404.2557  0.001   17432.8157   61375.6957   True
SawyerW StoneBr   85492.7567  0.001   52723.6789  118261.8345   True
SawyerW  Timber   49528.2968  0.001   22009.9275   77046.6661   True
SawyerW Veenker   72920.8817  0.001   28432.1064   117409.657   True
Somerst StoneBr    46088.501  0.001   14358.9461    77818.056   True
Somerst  Timber   10124.0411    0.9  -16147.8612   36395.9434  False
Somerst Veenker    33516.626 0.4563  -10212.1211   77245.3731  False
StoneBr  Timber  -35964.4599 0.0473  -71759.4474    -169.4724   True
StoneBr Veenker   -12571.875    0.9  -62604.5804   37460.8304  False
 Timber Veenker   23392.5849    0.9   -23369.791   70154.9608  False
--------------------------------------------------------------------


data["Neighborhood"].replace("Blueste","Blmngtn",inplace = True)
data["Neighborhood"].replace("Blmngtn","CollgCr",inplace = True)
data["Neighborhood"].replace("BrDale","BrkSide",inplace = True)
data["Neighborhood"].replace("BrkSide","Edwards",inplace = True)
data["Neighborhood"].replace("ClearCr","Crawfor",inplace = True)
data["Neighborhood"].replace("Gilbert","CollgCr",inplace = True)
data["Neighborhood"].replace("NWAmes","CollgCr",inplace = True)
data["Neighborhood"].replace("SawyerW","CollgCr",inplace = True)
data["Neighborhood"].replace("NPkVill","Edwards",inplace = True)
data["Neighborhood"].replace("OldTown","Edwards",inplace = True)
data["Neighborhood"].replace("MeadowV","IDOTRR",inplace = True)
data["Neighborhood"].replace("SWISU","Mitchel",inplace = True)
data["Neighborhood"].replace("Mitchel","NAmes",inplace = True)
data["Neighborhood"].replace("Sawyer","NAmes",inplace = True)
data["Neighborhood"].replace("NoRidge","NridgHt",inplace = True)
data["Neighborhood"].replace("Timber","Somerst",inplace = True)
data["Neighborhood"].replace("Veenker","StoneBr",inplace = True)
data["Neighborhood"].replace("Crawfor","Somerst",inplace = True)


data.Neighborhood.value_counts()

CollgCr    564
NAmes      534
Edwards    408
Somerst    281
NridgHt    164
IDOTRR      75
StoneBr     47
Name: Neighborhood, dtype: int64


mod = ols("Property_Sale_Price~Neighborhood",data =data).fit()
aov_table_Neighborhood = sm.stats.anova_lm(mod)
print(aov_table_Neighborhood)

                  df        sum_sq       mean_sq           F  PR(>F)
Neighborhood     6.0  5.331338e+12  8.885564e+11  461.536006     0.0
Residual      2066.0  3.977496e+12  1.925216e+09         NaN     NaN


t_Neighborhood = pairwise_tukeyhsd(data.Property_Sale_Price, data.Neighborhood, alpha = 0.05)
print(t_Neighborhood)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05        
==================================================================
 group1  group2   meandiff  p-adj     lower        upper    reject
------------------------------------------------------------------
CollgCr Edwards -66982.7862  0.001  -75398.7161 -58566.8563   True
CollgCr  IDOTRR -93969.5389  0.001 -109884.9865 -78054.0914   True
CollgCr   NAmes -47063.2398  0.001  -54881.8507 -39244.6289   True
CollgCr NridgHt   99918.032  0.001   88430.0743 111405.9896   True
CollgCr Somerst  29164.6515  0.001   19709.3745  38619.9284   True
CollgCr StoneBr  73894.0089  0.001   54234.5873  93553.4304   True
Edwards  IDOTRR -26986.7527  0.001  -43255.4008 -10718.1047   True
Edwards   NAmes  19919.5464  0.001    11404.964  28434.1288   True
Edwards NridgHt 166900.8181  0.001  154928.3244 178873.3119   True
Edwards Somerst  96147.4377  0.001   86109.0265 106185.8489   True
Edwards StoneBr 140876.7951  0.001  120930.3595 160823.2307   True
 IDOTRR   NAmes  46906.2992  0.001   30938.4656  62874.1327   True
 IDOTRR NridgHt 193887.5709  0.001  175837.2467 211937.8951   True
 IDOTRR Somerst 123134.1904  0.001  106304.3569 139964.0239   True
 IDOTRR StoneBr 167863.5478  0.001  143773.4277 191953.6679   True
  NAmes NridgHt 146981.2717  0.001  135420.8475 158541.6959   True
  NAmes Somerst  76227.8912  0.001      66684.7  85771.0825   True
  NAmes StoneBr 120957.2486  0.001  101255.3936 140659.1037   True
NridgHt Somerst -70753.3805  0.001  -83477.9627 -58028.7982   True
NridgHt StoneBr -26024.0231 0.0064  -47448.4388  -4599.6074   True
Somerst StoneBr  44729.3574  0.001   24322.6262  65136.0886   True
------------------------------------------------------------------


sns.countplot(data.Neighborhood)
plt.title('Count plot of Neighborhood')

Text(0.5, 1.0, 'Count plot of Neighborhood')


sns.catplot(x = 'Neighborhood', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Neighborhood vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Neighborhood vs Property_Sale_Price')


sns.boxplot(x = 'Neighborhood', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Neighborhood vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Neighborhood vs Property_Sale_Price')


data.Condition1.isnull().sum()

0


data.Condition1.value_counts()

Norm      1794
Feedr      112
Artery      62
RRAn        39
PosN        24
RRAe        15
PosA        13
RRNn        12
RRNe         2
Name: Condition1, dtype: int64


data.Condition1.value_counts().sum()

2073


sns.countplot(data.Condition1)
plt.title('Count plot of Condition1')

Text(0.5, 1.0, 'Count plot of Condition1')


data.groupby('Condition1')['Property_Sale_Price'].describe()


mod_Condition1= ols("Property_Sale_Price~Condition1",data =data).fit()
aov_table_Condition1 = sm.stats.anova_lm(mod_Condition1,type = 2)
print(aov_table_Condition1)

                df        sum_sq       mean_sq          F        PR(>F)
Condition1     8.0  3.908084e+11  4.885105e+10  11.306153  8.731315e-16
Residual    2064.0  8.918026e+12  4.320749e+09        NaN           NaN


tukey_Condition1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition1, alpha = 0.05)
print(tukey_Condition1)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
Artery  Feedr   10247.487    0.9  -22061.0574  42556.0314  False
Artery   Norm  48799.3665  0.001    22434.259   75164.474   True
Artery   PosA  110383.871  0.001   48123.6389  172644.103   True
Artery   PosN  78029.7043  0.001   28962.0401 127097.3685   True
Artery   RRAe  16817.2043    0.9  -41911.6478  75546.0564  False
Artery   RRAn  52731.9222 0.0029    11018.083  94445.7615   True
Artery   RRNe   59133.871    0.9  -87497.4573 205765.1992  False
Artery   RRNn   73383.871 0.0122    9014.8333 137752.9086   True
 Feedr   Norm  38551.8795  0.001     18673.14  58430.6189   True
 Feedr   PosA 100136.3839  0.001   40333.5254 159939.2424   True
 Feedr   PosN  67782.2173  0.001   21872.7296 113691.7049   True
 Feedr   RRAe   6569.7173    0.9  -49547.3342  62686.7687  False
 Feedr   RRAn  42484.4352 0.0152    4535.9205    80432.95   True
 Feedr   RRNe  48886.3839    0.9  -96718.5329 194491.3008  False
 Feedr   RRNn  63136.3839 0.0422    1141.0744 125131.6934   True
  Norm   PosA  61584.5045 0.0221     4772.017  118396.992   True
  Norm   PosN  29230.3378 0.4329  -12709.5943  71170.2699  False
  Norm   RRAe -31982.1622  0.612  -84900.9709  20936.6465  False
  Norm   RRAn   3932.5557    0.9  -29103.2827  36968.3942  False
  Norm   RRNe  10334.5045    0.9 -134067.9493 154736.9583  False
  Norm   RRNn  24584.5045    0.9   -34531.443   83700.452  False
  PosA   PosN -32354.1667 0.8831 -102640.5488  37932.2154  False
  PosA   RRAe -93566.6667 0.0055 -170907.5867 -16225.7466   True
  PosA   RRAn -57651.9487 0.1346 -123016.9564   7713.0589  False
  PosA   RRNe    -51250.0    0.9 -206276.7276 103776.7276  False
  PosA   RRNn    -37000.0 0.8971 -118706.2596  44706.2596  False
  PosN   RRAe    -61212.5 0.1076 -128390.7486   5965.7486  False
  PosN   RRAn -25297.7821 0.8503  -78249.4902  27653.9261  False
  PosN   RRNe -18895.8333    0.9 -169110.9639 131319.2972  False
  PosN   RRNn  -4645.8333    0.9  -76806.8479  67515.1812  False
  RRAe   RRAn  35914.7179 0.6594  -26095.9731   97925.409  False
  RRAe   RRNe  42316.6667    0.9  -111325.866 195959.1993  False
  RRAe   RRNn  56566.6667 0.3939  -22481.7642 135615.0976  False
  RRAn   RRNe   6401.9487    0.9  -141574.381 154378.2784  False
  RRAn   RRNn  20651.9487    0.9   -46724.759  88028.6564  False
  RRNe   RRNn     14250.0    0.9  -141635.608  170135.608  False
----------------------------------------------------------------


data['Condition1'].replace('Artery', 'Feedr', inplace = True)
data['Condition1'].replace('RRAe', 'Feedr', inplace = True)
data['Condition1'].replace('RRNe', 'Feedr', inplace = True)
data['Condition1'].replace('RRAn', 'Norm', inplace = True)
data['Condition1'].replace('RRNn', 'PosN', inplace = True)
data['Condition1'].replace('PosA', 'PosN', inplace = True)


data.Condition1.value_counts()

Norm     1833
Feedr     191
PosN       49
Name: Condition1, dtype: int64


mod_Condition1= ols("Property_Sale_Price~Condition1",data =data).fit()
aov_table_Condition1 = sm.stats.anova_lm(mod_Condition1,type = 2)
print(aov_table_Condition1)

                df        sum_sq       mean_sq          F        PR(>F)
Condition1     2.0  3.681387e+11  1.840694e+11  42.616775  7.288981e-19
Residual    2070.0  8.940695e+12  4.319177e+09        NaN           NaN


tukey_Condition1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition1, alpha = 0.05)
print(tukey_Condition1)

    Multiple Comparison of Means - Tukey HSD, FWER=0.05    
===========================================================
group1 group2  meandiff  p-adj   lower      upper    reject
-----------------------------------------------------------
 Feedr   Norm 40934.1146 0.001 29214.1142  52654.115   True
 Feedr   PosN 77526.7844 0.001 52843.0317 102210.537   True
  Norm   PosN 36592.6698 0.001 14280.0366  58905.303   True
-----------------------------------------------------------


sns.countplot(data.Condition1)
plt.title('Count plot of Condition1')

Text(0.5, 1.0, 'Count plot of Condition1')


sns.catplot(x = 'Condition1', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Condition1 vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Condition1 vs Property_Sale_Price')


sns.boxplot(x = 'Condition1', y = 'Property_Sale_Price', data = data)
plt.title('Box plot of Condition1 vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of Condition1 vs Property_Sale_Price')


data.Condition2.isnull().sum()

0


data.Condition2.value_counts()

Norm      2054
Feedr        9
RRNn         2
PosN         2
RRAn         2
Artery       2
RRAe         1
PosA         1
Name: Condition2, dtype: int64


data.Condition2.value_counts().sum()

2073


sns.countplot(data.Condition2)
plt.title('Count plot of Condition2')

Text(0.5, 1.0, 'Count plot of Condition2')


data.groupby('Condition2')['Property_Sale_Price'].describe()


mod = ols('Property_Sale_Price~Condition2', data = data).fit()
anova_Cond2 = sm.stats.anova_lm(mod)
print(anova_Cond2)

                df        sum_sq       mean_sq         F    PR(>F)
Condition2     7.0  8.550808e+10  1.221544e+10  2.734901  0.007943
Residual    2065.0  9.223326e+12  4.466502e+09       NaN       NaN


tukey_Cond2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition2)
print(tukey_Cond2)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
Artery  Feedr  20944.4444    0.9 -137565.6618 179454.5507  False
Artery   Norm  71339.5024 0.7772  -72108.0705 214787.0754  False
Artery   PosA    218500.0 0.1321  -29837.6097 466837.6097  False
Artery   PosN    155875.0 0.2765  -46891.8092 358641.8092  False
Artery   RRAe     83500.0    0.9 -164837.6097 331837.6097  False
Artery   RRAn     30405.0    0.9 -172361.8092 233171.8092  False
Artery   RRNn     -9750.0    0.9 -212516.8092 193016.8092  False
 Feedr   Norm   50395.058  0.318  -17341.7936 118131.9096  False
 Feedr   PosA 197555.5556 0.0942  -16179.4281 411290.5392  False
 Feedr   PosN 134930.5556 0.1629  -23579.5507 293440.6618  False
 Feedr   RRAe  62555.5556    0.9 -151179.4281 276290.5392  False
 Feedr   RRAn   9460.5556    0.9 -149049.5507 167970.6618  False
 Feedr   RRNn -30694.4444    0.9 -189204.5507 127815.6618  False
  Norm   PosA 147160.4976 0.3518  -55655.6646 349976.6598  False
  Norm   PosN  84535.4976 0.6106  -58912.0754 227983.0705  False
  Norm   RRAe  12160.4976    0.9 -190655.6646 214976.6598  False
  Norm   RRAn -40934.5024    0.9 -184382.0754 102513.0705  False
  Norm   RRNn -81089.5024 0.6541 -224537.0754  62358.0705  False
  PosA   PosN    -62625.0    0.9 -310962.6097 185712.6097  False
  PosA   RRAe   -135000.0 0.8253 -421755.5716 151755.5716  False
  PosA   RRAn   -188095.0 0.2952 -436432.6097  60242.6097  False
  PosA   RRNn   -228250.0 0.0982 -476587.6097  20087.6097  False
  PosN   RRAe    -72375.0    0.9 -320712.6097 175962.6097  False
  PosN   RRAn   -125470.0 0.5572 -328236.8092  77296.8092  False
  PosN   RRNn   -165625.0 0.2052 -368391.8092  37141.8092  False
  RRAe   RRAn    -53095.0    0.9 -301432.6097 195242.6097  False
  RRAe   RRNn    -93250.0    0.9 -341587.6097 155087.6097  False
  RRAn   RRNn    -40155.0    0.9 -242921.8092 162611.8092  False
----------------------------------------------------------------


data['Condition2'].replace('Artery', 'Feedr', inplace = True)
data['Condition2'].replace('RRAe', 'Feedr', inplace = True)
data['Condition2'].replace('RRAn', 'Feedr', inplace = True)
data['Condition2'].replace('RRNn', 'Feedr', inplace = True)
data['Condition2'].replace('PosA', 'PosN', inplace = True)


data.Condition2.value_counts()

Norm     2054
Feedr      16
PosN        3
Name: Condition2, dtype: int64


mod = ols('Property_Sale_Price~Condition2', data = data).fit()
anova_Cond2 = sm.stats.anova_lm(mod)
print(anova_Cond2)

                df        sum_sq       mean_sq         F    PR(>F)
Condition2     2.0  7.606935e+10  3.803467e+10  8.527432  0.000205
Residual    2070.0  9.232765e+12  4.460273e+09       NaN       NaN


tukey_Cond2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.Condition2)
print(tukey_Cond2)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1 group2   meandiff  p-adj    lower       upper    reject
--------------------------------------------------------------
 Feedr   Norm  51757.6274 0.0058 12445.5959  91069.6589   True
 Feedr   PosN  157168.125  0.001 58618.1072 255718.1428   True
  Norm   PosN 105410.4976 0.0175 14908.7794 195912.2157   True
--------------------------------------------------------------


sns.countplot(data.Condition2)
plt.title('Count plot of Condition2')

Text(0.5, 1.0, 'Count plot of Condition2')


sns.catplot(x = 'Condition2', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Condition2 vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Condition2 vs Property_Sale_Price')


sns.boxplot(x = 'Condition2', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Condition2 vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Condition2 vs Property_Sale_Price')


data.Dwelling_Type.isnull().sum()

0


data.Dwelling_Type.value_counts()

1Fam      1719
TwnhsE     168
Duplex      87
Twnhs       55
2fmCon      44
Name: Dwelling_Type, dtype: int64


data.Dwelling_Type.value_counts().sum()

2073


sns.countplot(data.Dwelling_Type)
plt.title('Count plot of Dwelling_Type')

Text(0.5, 1.0, 'Count plot of Dwelling_Type')


data.groupby('Dwelling_Type')['Property_Sale_Price'].describe()


mod_Dwelling_Type = ols('Property_Sale_Price~Dwelling_Type', data = data).fit()
anova_Dwelling_Type = sm.stats.anova_lm(mod)
print(anova_Dwelling_Type)

                df        sum_sq       mean_sq         F    PR(>F)
Condition2     2.0  7.606935e+10  3.803467e+10  8.527432  0.000205
Residual    2070.0  9.232765e+12  4.460273e+09       NaN       NaN


tukey_Dwelling_Type = pairwise_tukeyhsd(data.Property_Sale_Price, data.Dwelling_Type)
print(tukey_Dwelling_Type)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1 group2   meandiff  p-adj    lower       upper    reject
--------------------------------------------------------------
  1Fam 2fmCon -53764.0501 0.001 -81102.5467 -26425.5536   True
  1Fam Duplex -49265.1504 0.001 -68942.8378 -29587.4631   True
  1Fam  Twnhs -45439.5047 0.001 -69967.9641 -20911.0453   True
  1Fam TwnhsE    808.8974   0.9 -13665.6875  15283.4823  False
2fmCon Duplex   4498.8997   0.9 -28626.5994  37624.3988  False
2fmCon  Twnhs   8324.5455   0.9 -27893.3042  44542.3951  False
2fmCon TwnhsE  54572.9475 0.001  24248.0251  84897.8699   True
Duplex  Twnhs   3825.6458   0.9 -27021.5684  34672.8599  False
Duplex TwnhsE  50074.0478 0.001  26422.0097  73726.0859   True
 Twnhs TwnhsE  46248.4021 0.001  18430.1913  74066.6128   True
--------------------------------------------------------------


data['Dwelling_Type'].replace('1Fam', 'TwnhsE', inplace = True)
data['Dwelling_Type'].replace('2fmCon', 'Duplex', inplace = True)
data['Dwelling_Type'].replace('Twnhs', 'Duplex', inplace = True)


data.Dwelling_Type.value_counts()

TwnhsE    1887
Duplex     186
Name: Dwelling_Type, dtype: int64


data.Dwelling_Type.value_counts().sum()

2073


df1=data[data.Dwelling_Type=='TwnhsE']

df2=data[data.Dwelling_Type=='Duplex']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=9.780816776783727, pvalue=4.0657590717491367e-22)


sns.countplot(data.Dwelling_Type)
plt.title('Count plot of Dwelling_Type')

Text(0.5, 1.0, 'Count plot of Dwelling_Type')


sns.catplot(x = 'Dwelling_Type', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of Dwelling_Type vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Dwelling_Type vs Property_Sale_Price')


sns.boxplot(x = 'Dwelling_Type', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Dwelling_Type vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Dwelling_Type vs Property_Sale_Price')


data.HouseStyle.isnull().sum()

0


data.HouseStyle.value_counts()

1Story    1036
2Story     638
1.5Fin     214
SLvl        89
SFoyer      54
2.5Unf      16
1.5Unf      16
2.5Fin      10
Name: HouseStyle, dtype: int64


data.HouseStyle.value_counts().sum()

2073


sns.countplot(data.HouseStyle)
plt.title('Count plot of HouseStyle')

Text(0.5, 1.0, 'Count plot of HouseStyle')


data.groupby("HouseStyle")["Property_Sale_Price"].describe()


mod_HouseStyle= ols("Property_Sale_Price~HouseStyle",data =data).fit()
aov_table_HouseStyle = sm.stats.anova_lm(mod_HouseStyle,type = 2)
print(aov_table_HouseStyle)

                df        sum_sq       mean_sq          F        PR(>F)
HouseStyle     7.0  8.847505e+11  1.263929e+11  30.982766  4.870702e-41
Residual    2065.0  8.424084e+12  4.079459e+09        NaN           NaN


tukey_HouseStyle = pairwise_tukeyhsd(data.Property_Sale_Price, data.HouseStyle, alpha = 0.05)
print(tukey_HouseStyle)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
1.5Fin 1.5Unf -17183.1893    0.9  -67407.2079  33040.8294  False
1.5Fin 1Story  28950.2297  0.001   14399.5707  43500.8886   True
1.5Fin 2.5Fin  50523.0607 0.2203  -12171.7423 113217.8638  False
1.5Fin 2.5Unf  12082.4357    0.9  -38141.5829  62306.4544  False
1.5Fin 2Story  60661.9322  0.001   45353.9838  75969.8807   True
1.5Fin SFoyer  -6837.0133    0.9  -36347.6141  22673.5874  False
1.5Fin   SLvl  20998.6787  0.154   -3443.1583  45440.5158  False
1.5Unf 1Story  46133.4189 0.0802   -2684.8551   94951.693  False
1.5Unf 2.5Fin    67706.25 0.1457  -10409.9489 145822.4489  False
1.5Unf 2.5Unf   29265.625    0.9  -39246.8137  97778.0637  False
1.5Unf 2Story  77845.1215  0.001   28795.8046 126894.4384   True
1.5Unf SFoyer  10346.1759    0.9  -44811.5775  65503.9293  False
1.5Unf   SLvl   38181.868 0.3518  -14438.5235  90802.2595  False
1Story 2.5Fin  21572.8311    0.9  -40001.5967  83147.2589  False
1Story 2.5Unf -16867.7939    0.9   -65686.068  31950.4801  False
1Story 2Story  31711.7026  0.001   21959.5176  41463.8875   True
1Story SFoyer  -35787.243 0.0016  -62836.2242  -8738.2618   True
1Story   SLvl  -7951.5509    0.9  -29356.5789  13453.4771  False
2.5Fin 2.5Unf  -38440.625 0.7867 -116556.8239  39675.5739  False
2.5Fin 2Story  10138.8715    0.9  -51618.8954  71896.6383  False
2.5Fin SFoyer -57360.0741 0.1532 -124072.6219   9352.4737  False
2.5Fin   SLvl  -29524.382 0.8506  -94154.8074  35106.0433  False
2.5Unf 2Story  48579.4965 0.0545    -469.8204  97628.8134  False
2.5Unf SFoyer -18919.4491    0.9  -74077.2025  36238.3043  False
2.5Unf   SLvl    8916.243    0.9  -43704.1485  61536.6345  False
2Story SFoyer -67498.9455  0.001  -94962.7215 -40035.1696   True
2Story   SLvl -39663.2535  0.001  -61590.1054 -17736.4016   True
SFoyer   SLvl  27835.6921 0.1845    -5590.781  61262.1651  False
----------------------------------------------------------------


data["HouseStyle"].replace("1.5Unf","1.5Fin",inplace = True)
data["HouseStyle"].replace("2.5Unf","1.5Fin",inplace = True)
data["HouseStyle"].replace("SFoyer","1.5Fin",inplace = True)
data["HouseStyle"].replace("2.5Fin","1.2Story",inplace = True)
data["HouseStyle"].replace('1.2Story','2Story',inplace = True)
data["HouseStyle"].replace('SLvl','1Story',inplace = True)


data.HouseStyle.value_counts()

1Story    1125
2Story     648
1.5Fin     300
Name: HouseStyle, dtype: int64


data.HouseStyle.value_counts().sum()

2073


mod_HouseStyle= ols("Property_Sale_Price~HouseStyle",data =data).fit()
aov_table_HouseStyle = sm.stats.anova_lm(mod_HouseStyle,type = 2)
print(aov_table_HouseStyle)

                df        sum_sq       mean_sq           F        PR(>F)
HouseStyle     2.0  8.696497e+11  4.348248e+11  106.655731  8.210510e-45
Residual    2070.0  8.439185e+12  4.076901e+09         NaN           NaN


tukey_HouseStyle = pairwise_tukeyhsd(data.Property_Sale_Price, data.HouseStyle, alpha = 0.05)
print(tukey_HouseStyle)

    Multiple Comparison of Means - Tukey HSD, FWER=0.05    
===========================================================
group1 group2  meandiff  p-adj   lower      upper    reject
-----------------------------------------------------------
1.5Fin 1Story 29823.8762 0.001 20092.9221 39554.8303   True
1.5Fin 2Story 62008.1707 0.001 51550.3566 72465.9849   True
1Story 2Story 32184.2945 0.001 24798.8697 39569.7194   True
-----------------------------------------------------------


sns.countplot(data.HouseStyle)
plt.title('Count plot of HouseStyle')

Text(0.5, 1.0, 'Count plot of HouseStyle')


sns.catplot(x = 'HouseStyle', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs HouseStyle')

Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs HouseStyle')


sns.boxplot(x = 'HouseStyle', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Sale_Price vs HouseStyle')

Text(0.5, 1.0, 'Box Plot of Property_Sale_Price vs HouseStyle')


data.OverallQual.isnull().sum()

0


data.OverallQual.describe()

count    2073.000000
mean        6.233478
std         1.517115
min         1.000000
25%         5.000000
50%         6.000000
75%         7.000000
max        10.000000
Name: OverallQual, dtype: float64


data.OverallQual.value_counts()

5     508
6     463
7     416
8     265
4     214
9     164
3      20
10     17
2       4
1       2
Name: OverallQual, dtype: int64


data.OverallQual.value_counts().sum()

2073


sns.countplot(data.OverallQual)
plt.title('Count plot of OverallQual')

Text(0.5, 1.0, 'Count plot of OverallQual')


data.groupby("OverallQual")["Property_Sale_Price"].describe()


mod_OverallQual= ols("Property_Sale_Price~OverallQual",data =data).fit()
aov_table_OverallQual = sm.stats.anova_lm(mod_OverallQual,type = 2)
print(aov_table_OverallQual)

                 df        sum_sq       mean_sq           F         PR(>F)
OverallQual     1.0  2.676231e+12  2.676231e+12  835.640689  1.193209e-154
Residual     2071.0  6.632604e+12  3.202609e+09         NaN            NaN


tukey_OverallQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallQual, alpha = 0.05)
print(tukey_OverallQual)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
     1      2     -2494.5    0.9 -153629.5891 148640.5891  False
     1      3    37323.75    0.9   -92100.608  166748.108  False
     1      4  86517.6729  0.453  -37458.9111 210494.2569  False
     1      5  92476.1378 0.3459   -31167.823 216120.0986  False
     1      6  116189.905  0.087   -7477.6168 239857.4267  False
     1      7 152171.1635  0.004   28473.5979  275868.729   True
     1      8 187154.9811  0.001   63288.9077 311021.0546   True
     1      9  162740.189 0.0014   38588.7388 286891.6392   True
     1     10    269100.0  0.001  138641.6094 399558.3906   True
     2      3    39818.25    0.9  -55767.9732 135404.4732  False
     2      4  89012.1729 0.0452     942.5698  177081.776   True
     2      5  94970.6378 0.0214     7369.892 182571.3836   True
     2      6  118684.405  0.001   31050.4073 206318.4027   True
     2      7 154665.6635  0.001   66989.2736 242342.0533   True
     2      8 189649.4811  0.001  101735.5132 277563.4491   True
     2      9  165234.689  0.001   76919.0947 253550.2834   True
     2     10    271594.5  0.001  174612.7837 368576.2163   True
     3      4  49193.9229 0.0054    8388.2336  89999.6122   True
     3      5  55152.3878  0.001   15368.7235  94936.0521   True
     3      6   78866.155  0.001   39009.3257 118722.9843   True
     3      7 114847.4135  0.001    74897.462 154797.3649   True
     3      8 149831.2311  0.001  109362.5389 190299.9233   True
     3      9  125416.439  0.001   84082.5124 166750.3657   True
     3     10   231776.25  0.001    174206.27   289346.23   True
     4      5   5958.4649    0.9   -8263.6614  20180.5912  False
     4      6  29672.2321  0.001   15246.7071   44097.757   True
     4      7  65653.4906  0.001   50972.6339  80334.3472   True
     4      8 100637.3082  0.001   84598.4811 116676.1354   True
     4      9  76222.5161  0.001   58111.1269  94333.9054   True
     4     10 182582.3271  0.001  138606.9758 226557.6784   True
     5      6  23713.7672  0.001   12500.7642  34926.7702   True
     5      7  59695.0257  0.001   48155.3889  71234.6624   True
     5      8  94678.8433  0.001   81454.6366   107903.05   True
     5      9  70264.0512  0.001   54590.5677  85937.5347   True
     5     10 176623.8622  0.001  133595.1836 219652.5408   True
     6      7  35981.2585  0.001   24191.8518  47770.6652   True
     6      8  70965.0762  0.001    57522.363  84407.7893   True
     6      9  46550.2841  0.001   30692.0061   62408.562   True
     6     10  152910.095  0.001  109813.7602 196006.4299   True
     7      8  34983.8177  0.001   21267.4655  48700.1699   True
     7      9  10569.0256 0.5357    -5521.865  26659.9162  False
     7     10 116928.8365  0.001   73746.3649 160111.3082   True
     8      9 -24414.7921  0.001  -41753.5691  -7076.0152   True
     8     10  81945.0189  0.001   38282.1939 125607.8439   True
     9     10  106359.811  0.001   61893.8608 150825.7612   True
----------------------------------------------------------------


data["OverallQual"].replace(1, 3,inplace = True)
data["OverallQual"].replace(2, 3 ,inplace = True)
data["OverallQual"].replace(4, 5,inplace = True)
data["OverallQual"].replace(9, 7,inplace = True)


data.OverallQual.value_counts()

5     722
7     580
6     463
8     265
3      26
10     17
Name: OverallQual, dtype: int64


data.OverallQual.value_counts().sum()

2073


mod_OverallQual= ols("Property_Sale_Price~OverallQual",data =data).fit()
aov_table_OverallQual = sm.stats.anova_lm(mod_OverallQual,type = 2)
print(aov_table_OverallQual)

                 df        sum_sq       mean_sq           F         PR(>F)
OverallQual     1.0  2.998150e+12  2.998150e+12  983.913616  4.758552e-177
Residual     2071.0  6.310684e+12  3.047168e+09         NaN            NaN


tukey_OverallQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallQual, alpha = 0.05)
print(tukey_OverallQual)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1 group2   meandiff  p-adj    lower       upper    reject
--------------------------------------------------------------
     3      5  62383.2477 0.001  30976.7078  93789.7876   True
     3      6  87863.0973 0.001  56152.6895  119573.505   True
     3      7 126832.8389 0.001  95292.9464 158372.7313   True
     3      8 158828.1734 0.001 126494.0234 191162.3235   True
     3     10 240773.1923 0.001 191699.6216  289846.763   True
     5      6  25479.8496 0.001  16112.3292    34847.37   True
     5      7  64449.5912 0.001  55676.6046  73222.5777   True
     5      8  96444.9257 0.001  85144.5832 107745.2682   True
     5     10 178389.9446 0.001 139784.0406 216995.8486   True
     6      7  38969.7416 0.001  29164.4113  48775.0719   True
     6      8  70965.0762 0.001  58845.7961  83084.3563   True
     6     10  152910.095 0.001 114056.5877 191763.6023   True
     7      8  31995.3346 0.001  20329.4954  43661.1738   True
     7     10 113940.3534 0.001  75225.8874 152654.8195   True
     8     10  81945.0189 0.001  42580.7923 121309.2454   True
--------------------------------------------------------------


sns.countplot(data.OverallQual)
plt.title('Count plot of OverallQual')

Text(0.5, 1.0, 'Count plot of OverallQual')


sns.catplot(x = 'OverallQual', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs OverallQual')

Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs OverallQual')


sns.boxplot(x = 'OverallQual', y = 'Property_Sale_Price', data = data)
plt.title('Box plot of Property_Sale_Price vs OverallQual')

Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs OverallQual')


data.OverallCond.isnull().sum()

0


data.OverallCond.describe()

count    2073.000000
mean        5.835022
std         1.360214
min         1.000000
25%         5.000000
50%         5.000000
75%         7.000000
max         9.000000
Name: OverallCond, dtype: float64


data.OverallCond.value_counts()

5    943
6    351
7    315
8    172
4    147
9    113
3     26
2      5
1      1
Name: OverallCond, dtype: int64


data.OverallCond.value_counts().sum()

2073


sns.countplot(data.OverallCond)
plt.title('Count plot of OverallCond')

Text(0.5, 1.0, 'Count plot of OverallCond')


data.groupby("OverallCond")["Property_Sale_Price"].describe()


mod_OverallCond= ols("Property_Sale_Price~OverallCond",data =data).fit()
aov_table_OverallCond = sm.stats.anova_lm(mod_OverallCond,type = 2)
print(aov_table_OverallCond)

                 df        sum_sq       mean_sq         F    PR(>F)
OverallCond     1.0  3.136673e+10  3.136673e+10  7.001964  0.008204
Residual     2071.0  9.277467e+12  4.479704e+09       NaN       NaN


tukey_OverallCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallCond, alpha = 0.05)
print(tukey_OverallCond)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
     1      2     70100.0    0.9 -149891.1778 290091.1778  False
     1      3  38367.1538    0.9 -166281.9513  243016.259  False
     1      4 102496.2041 0.7921  -99009.2624 304001.6705  False
     1      5 133661.3372 0.4974  -67268.6667 334591.3411  False
     1      6  97068.6154  0.841 -104040.8055 298178.0363  False
     1      7 107894.0317 0.7399  -93248.0341 309036.0976  False
     1      8 103482.6744 0.7824  -97923.8198 304889.1686  False
     1      9  118425.177 0.6445  -83285.0164 320135.3704  False
     2      3 -31732.8462    0.9 -129800.0545  66334.3622  False
     2      4  32396.2041    0.9  -58929.4447 123721.8529  False
     2      5  63561.3372 0.4143  -26487.4695 153610.1439  False
     2      6  26968.6154    0.9  -63479.8245 117417.0553  False
     2      7  37794.0317    0.9  -52726.9701 128315.0336  False
     2      8  33382.6744    0.9  -57724.3892  124489.738  False
     2      9   48325.177 0.7584  -43451.3083 140101.6623  False
     3      4  64129.0502  0.001   21403.0388 106855.0616   True
     3      5  95294.1834  0.001   55370.1868   135218.18   True
     3      6  58701.4615  0.001   17884.0853  99518.8378   True
     3      7  69526.8779  0.001   28548.9607 110504.7951   True
     3      8  65115.5206  0.001   22858.7465 107372.2947   True
     3      9  80058.0231  0.001   36376.6637 123739.3826   True
     4      5  31165.1331  0.001   13357.2057  48973.0606   True
     4      6  -5427.5887    0.9  -25157.1345  14301.9571  False
     4      7   5397.8277    0.9  -14661.7454  25457.4007  False
     4      8    986.4703    0.9  -21570.8285  23543.7691  False
     4      9  15928.9729 0.5563   -9195.8741    41053.82  False
     5      6 -36592.7218  0.001   -49149.331 -24036.1127   True
     5      7 -25767.3055  0.001    -38836.35 -12698.2609   True
     5      8 -30178.6628  0.001   -46829.344 -13527.9816   True
     5      9 -15236.1602 0.3033  -35227.9434   4755.6229  False
     6      7  10825.4164 0.4379   -4760.8797  26411.7124  False
     6      8    6414.059    0.9  -12277.5937  25105.7118  False
     6      9  21356.5616 0.0584    -364.4902  43077.6135  False
     7      8  -4411.3573    0.9  -23451.0363  14628.3217  False
     7      9  10531.1452 0.8494  -11490.1078  32552.3983  False
     8      9  14942.5026  0.593   -9375.8207  39260.8258  False
----------------------------------------------------------------


data["OverallCond"].replace(1, 3,inplace = True)
data["OverallCond"].replace(2, 3,inplace = True)
data["OverallCond"].replace(4, 6,inplace = True)
data["OverallCond"].replace(7, 6,inplace = True)
data["OverallCond"].replace(8, 6,inplace = True)
data["OverallCond"].replace(9, 5,inplace = True)


data.OverallCond.value_counts()

5    1056
6     985
3      32
Name: OverallCond, dtype: int64


mod_OverallCond= ols("Property_Sale_Price~OverallCond",data =data).fit()
aov_table_OverallCond = sm.stats.anova_lm(mod_OverallCond,type = 2)
print(aov_table_OverallCond)

                 df        sum_sq       mean_sq        F        PR(>F)
OverallCond     1.0  1.190641e+11  1.190641e+11  26.8322  2.434174e-07
Residual     2071.0  9.189770e+12  4.437359e+09      NaN           NaN


tukey_OverallCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.OverallCond, alpha = 0.05)
print(tukey_OverallCond)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05     
=============================================================
group1 group2  meandiff  p-adj    lower       upper    reject
-------------------------------------------------------------
     3      5 89904.5152 0.001  62647.6117 117161.4186   True
     3      6 60334.1371 0.001  33048.3561  87619.9181   True
     5      6 -29570.378 0.001 -36299.2218 -22841.5343   True
-------------------------------------------------------------


sns.countplot(data.OverallCond)
plt.title('Count plot of OverallCond')

Text(0.5, 1.0, 'Count plot of OverallCond')


sns.catplot(x = 'OverallCond', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs OverallCond')

Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs OverallCond')


sns.boxplot(x = 'OverallCond', y = 'Property_Sale_Price', data = data)
plt.title('Box plot of Property_Sale_Price vs OverallCond')

Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs OverallCond')


data.YearBuilt.isnull().sum()

0


data.YearBuilt.value_counts()

2005    100
2006     86
2004     63
2007     58
1977     56
       ... 
1898      1
1903      1
1913      1
1911      1
1872      1
Name: YearBuilt, Length: 117, dtype: int64


data.YearBuilt.value_counts().sum()

2073


plt.hist(data.YearBuilt)
plt.title('Hist of YearBuilt')

Text(0.5, 1.0, 'Hist of YearBuilt')


sns.distplot(data.YearBuilt)
plt.title('Mormality of YearBuilt')

Text(0.5, 1.0, 'Mormality of YearBuilt')


sns.countplot(data.YearBuilt)
plt.title('Count plot of YearBuilt')

Text(0.5, 1.0, 'Count plot of YearBuilt')


sns.boxplot(x = data.YearBuilt, data =data, color = 'r')
plt.title('Box plot of YearBuilt')

Text(0.5, 1.0, 'Box plot of YearBuilt')


IQR = data.YearBuilt.quantile(.75) - data.YearBuilt.quantile(.25)
IQR

49.0


upper_limit = data.YearBuilt.quantile(.75) + (1.5*IQR)
upper_limit

2072.5


lower_limit = data.YearBuilt.quantile(.25) - (1.5*IQR)
lower_limit

1876.5


len(data.YearBuilt[data.YearBuilt > upper_limit])

0


len(data.YearBuilt[data.YearBuilt < lower_limit])

2


data['YearBuilt'] = np.where(data['YearBuilt'] < lower_limit, lower_limit, data['YearBuilt'])


sns.boxplot(x = data.YearBuilt, data =data, color = 'g')
plt.title('Box plot of YearBuilt')

Text(0.5, 1.0, 'Box plot of YearBuilt')


sns.distplot(data.YearBuilt)
plt.title('Normality of YearBuilt')

Text(0.5, 1.0, 'Normality of YearBuilt')


np.corrcoef(data.YearBuilt, data.Property_Sale_Price)

array([[1.        , 0.47415594],
       [0.47415594, 1.        ]])


sns.scatterplot(x = data['YearBuilt'], y = data.Property_Sale_Price, data = data )
plt.xlabel('YearBuilt')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of YearBuilt vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter plot of YearBuilt vs Property_Sale_Price')


data.YearRemodAdd.isnull().sum()

0


data.YearRemodAdd.value_counts()

1950    223
2006    138
2007    121
2005    107
2000     79
       ... 
2014      3
2019      2
2022      2
2012      2
2023      2
Name: YearRemodAdd, Length: 72, dtype: int64


data.YearRemodAdd.value_counts().sum()

2073


plt.hist(data.YearRemodAdd)
plt.title('Hist of YearRemodAdd')

Text(0.5, 1.0, 'Hist of YearRemodAdd')


sns.distplot(data.YearRemodAdd)
plt.title('Normality of YearRemodAdd')

Text(0.5, 1.0, 'Normality of YearRemodAdd')


sns.countplot(data.YearRemodAdd)
plt.title('Dist plot of YearRemodAdd')

Text(0.5, 1.0, 'Dist plot of YearRemodAdd')


sns.boxplot(x = data.YearRemodAdd, data =data, color = 'g')
plt.title('Box plot of YearRemodAdd')

Text(0.5, 1.0, 'Box plot of YearRemodAdd')


np.corrcoef(data.YearRemodAdd, data.Property_Sale_Price)

array([[1.        , 0.51542146],
       [0.51542146, 1.        ]])


sns.scatterplot(x = data['YearRemodAdd'], y = data.Property_Sale_Price, data = data )
plt.xlabel('YearRemodAdd')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of YearRemodAdd vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter plot of YearRemodAdd vs Property_Sale_Price')


data.RoofStyle.isnull().sum()

0


data.RoofStyle.value_counts()

Gable      1606
Hip         420
Flat         19
Gambrel      15
Mansard      11
Shed          2
Name: RoofStyle, dtype: int64


data.RoofStyle.value_counts().sum()

2073


sns.countplot(data.RoofStyle)
plt.title('Count plot of RoofStyle')

Text(0.5, 1.0, 'Count plot of RoofStyle')


data.groupby("RoofStyle")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ RoofStyle', data=data).fit()
print(sm.stats.anova_lm(mod))

               df        sum_sq       mean_sq          F        PR(>F)
RoofStyle     5.0  3.749304e+11  7.498607e+10  17.349214  7.395804e-17
Residual   2067.0  8.933904e+12  4.322160e+09        NaN           NaN


t_Roof_Style = pairwise_tukeyhsd(data.Property_Sale_Price, data.RoofStyle)
print(t_Roof_Style)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05        
==================================================================
 group1  group2   meandiff  p-adj     lower        upper    reject
------------------------------------------------------------------
   Flat   Gable  -27627.172 0.4541  -70901.5578  15647.2139  False
   Flat Gambrel -30386.3158 0.7361  -95155.8583  34383.2267  False
   Flat     Hip   5250.3866    0.9  -38732.5898   49233.363  False
   Flat Mansard -30153.7703 0.8097 -101200.0409  40892.5002  False
   Flat    Shed  26713.6842    0.9  -112689.165 166116.5334  False
  Gable Gambrel  -2759.1438    0.9  -51402.8782  45884.5906  False
  Gable     Hip  32877.5585  0.001   22600.3278  43154.7893   True
  Gable Mansard  -2526.5984    0.9  -59260.1142  54206.9174  False
  Gable    Shed  54340.8562 0.8342  -78340.2413 187021.9536  False
Gambrel     Hip  35636.7024 0.3074  -13638.4703  84911.8751  False
Gambrel Mansard    232.5455    0.9  -74206.1071   74671.198  False
Gambrel    Shed     57100.0 0.8425  -84061.9453 198261.9453  False
    Hip Mansard -35404.1569 0.4902  -92679.9944  21871.6806  False
    Hip    Shed  21463.2976    0.9 -111450.5969 154377.1922  False
Mansard    Shed  56867.4545 0.8589  -87282.3762 201017.2853  False
------------------------------------------------------------------


data["RoofStyle"].replace('Flat', 'Hip',inplace = True)
data["RoofStyle"].replace('Shed', 'Hip',inplace = True)
data["RoofStyle"].replace('Gambrel', 'Gable',inplace = True)
data["RoofStyle"].replace('Mansard', 'Gable',inplace = True)


data.RoofStyle.value_counts()

Gable    1632
Hip       441
Name: RoofStyle, dtype: int64


df1=data[data.RoofStyle=='Gable']

df2=data[data.RoofStyle=='Hip']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=-9.301767992439983, pvalue=3.396220835555546e-20)


sns.countplot(data.RoofStyle)
plt.title('Count plot of RoofStyle')

Text(0.5, 1.0, 'Count plot of RoofStyle')


sns.catplot(x = 'RoofStyle', y = 'Property_Sale_Price', data = data)
plt.title('Cat plot of Property_Sale_Price vs RoofStyle')

Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs RoofStyle')


sns.boxplot(x = 'RoofStyle', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of Property_Sale_Price vs RoofStyle')

Text(0.5, 1.0, 'Box Plot of Property_Sale_Price vs RoofStyle')


data.RoofMatl.isnull().sum()

0


data.RoofMatl.value_counts()

CompShg    2036
Tar&Grv      17
WdShngl      10
WdShake       6
Metal         1
Roll          1
ClyTile       1
Membran       1
Name: RoofMatl, dtype: int64


data.RoofMatl.value_counts().sum()

2073


sns.countplot(data.RoofMatl)
plt.title('Count Plot of RoofMatl')

Text(0.5, 1.0, 'Count Plot of RoofMatl')


data.groupby("RoofMatl")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ RoofMatl', data=data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq         F        PR(>F)
RoofMatl     7.0  2.033413e+11  2.904876e+10  6.587858  1.016746e-07
Residual  2065.0  9.105493e+12  4.409440e+09       NaN           NaN


t_RoofMatl = pairwise_tukeyhsd(data.Property_Sale_Price, data.RoofMatl)
print(t_RoofMatl)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05        
==================================================================
 group1  group2   meandiff  p-adj     lower        upper    reject
------------------------------------------------------------------
ClyTile CompShg  16628.9086    0.9 -184887.9765 218145.7938  False
ClyTile Membran     81500.0    0.9 -203417.9506 366417.9506  False
ClyTile   Metal     20000.0    0.9 -264917.9506 304917.9506  False
ClyTile    Roll    -23000.0    0.9 -307917.9506 261917.9506  False
ClyTile Tar&Grv  32702.3529    0.9 -174605.9068 240010.6126  False
ClyTile WdShake     89000.0    0.9 -128609.6792 306609.6792  False
ClyTile WdShngl    144450.0 0.4349  -66850.8074 355750.8074  False
CompShg Membran  64871.0914    0.9 -136645.7938 266387.9765  False
CompShg   Metal   3371.0914    0.9 -198145.7938 204887.9765  False
CompShg    Roll -39628.9086    0.9 -241145.7938 161887.9765  False
CompShg Tar&Grv  16073.4443    0.9   -32993.153  65140.0416  False
CompShg WdShake  72371.0914 0.1334   -9998.7389 154740.9216  False
CompShg WdShngl 127821.0914  0.001   63955.2347  191686.948   True
Membran   Metal    -61500.0    0.9 -346417.9506 223417.9506  False
Membran    Roll   -104500.0    0.9 -389417.9506 180417.9506  False
Membran Tar&Grv -48797.6471    0.9 -256105.9068 158510.6126  False
Membran WdShake      7500.0    0.9 -210109.6792 225109.6792  False
Membran WdShngl     62950.0    0.9 -148350.8074 274250.8074  False
  Metal    Roll    -43000.0    0.9 -327917.9506 241917.9506  False
  Metal Tar&Grv  12702.3529    0.9 -194605.9068 220010.6126  False
  Metal WdShake     69000.0    0.9 -148609.6792 286609.6792  False
  Metal WdShngl    124450.0 0.6112  -86850.8074 335750.8074  False
   Roll Tar&Grv  55702.3529    0.9 -151605.9068 263010.6126  False
   Roll WdShake    112000.0 0.7458 -105609.6792 329609.6792  False
   Roll WdShngl    167450.0 0.2394  -43850.8074 378750.8074  False
Tar&Grv WdShake  56297.6471 0.6121  -39370.7828 151966.0769  False
Tar&Grv WdShngl 111747.6471  0.001   31457.5033 192037.7908   True
WdShake WdShngl     55450.0 0.7126  -48587.3257 159487.3257  False
------------------------------------------------------------------


data["RoofMatl"].replace('ClyTile', 'CompShg',inplace = True)
data["RoofMatl"].replace('Membran', 'CompShg',inplace = True)
data["RoofMatl"].replace('Metal', 'CompShg',inplace = True)
data["RoofMatl"].replace('Roll', 'CompShg',inplace = True)
data["RoofMatl"].replace('Tar&Grv', 'CompShg',inplace = True)
data["RoofMatl"].replace('WdShake', 'WdShngl',inplace = True)


data.RoofMatl.value_counts()

CompShg    2057
WdShngl      16
Name: RoofMatl, dtype: int64


data.RoofMatl.value_counts().sum()

2073


df1=data[data.RoofMatl =='CompShg']

df2=data[data.RoofMatl =='WdShngl']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=-6.415414097229135, pvalue=1.7352766367863425e-10)


sns.countplot(data.RoofMatl)
plt.title('Count Plot of RoofMatl')

Text(0.5, 1.0, 'Count Plot of RoofMatl')


sns.catplot(x = 'RoofMatl', y = 'Property_Sale_Price', data = data)
plt.title('Cat Plot of RoofMatl vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of RoofMatl vs Property_Sale_Price')


sns.boxplot(x = 'RoofMatl', y = 'Property_Sale_Price', data = data)
plt.title('Box Plot of RoofMatl vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of RoofMatl vs Property_Sale_Price')


data.Exterior1st.isnull().sum()

0


data.Exterior1st.value_counts()

VinylSd    750
HdBoard    316
MetalSd    308
Wd Sdng    285
Plywood    156
CemntBd     87
BrkFace     72
WdShing     37
Stucco      28
AsbShng     24
Stone        3
BrkComm      3
AsphShn      2
CBlock       1
ImStucc      1
Name: Exterior1st, dtype: int64


data.Exterior1st.value_counts().sum()

2073


sns.countplot(data.Exterior1st)
plt.title('Count plot of Exterior1st')

Text(0.5, 1.0, 'Count plot of Exterior1st')


data.groupby("Exterior1st")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Exterior1st', data=data).fit()
print(sm.stats.anova_lm(mod))

                 df        sum_sq       mean_sq          F        PR(>F)
Exterior1st    14.0  1.658449e+12  1.184607e+11  31.866638  1.146734e-77
Residual     2058.0  7.650385e+12  3.717388e+09        NaN           NaN


t_Exterior1st = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior1st)
print(t_Exterior1st)

        Multiple Comparison of Means - Tukey HSD, FWER=0.05        
===================================================================
 group1  group2   meandiff   p-adj     lower        upper    reject
-------------------------------------------------------------------
AsbShng AsphShn   -3680.5417    0.9 -156049.3745 148688.2912  False
AsbShng BrkComm   -29013.875    0.9 -155792.4072  97764.6572  False
AsbShng BrkFace   83799.3194  0.001   35002.2396 132596.3992   True
AsbShng  CBlock    1319.4583    0.9 -209978.0954  212617.012  False
AsbShng CemntBd  110554.8032  0.001   62820.9681 158288.6382   True
AsbShng HdBoard   55227.8064 0.0018   11392.8699  99062.7429   True
AsbShng ImStucc  158319.4583 0.4134  -52978.0954  369617.012  False
AsbShng MetalSd   46673.2733 0.0245    2798.1703  90548.3762   True
AsbShng Plywood   69131.1186  0.001   23737.1223 114525.1149   True
AsbShng   Stone  164319.4583 0.0011   37540.9261 291097.9905   True
AsbShng  Stucco   57899.0655 0.0473     309.0599 115489.0711   True
AsbShng VinylSd   104409.349  0.001   61479.0105 147339.6875   True
AsbShng Wd Sdng   43480.4373 0.0568    -522.4602  87483.3347  False
AsbShng WdShing    56154.107 0.0342    1893.0162 110415.1978   True
AsphShn BrkComm  -25333.3333    0.9 -214323.6107 163656.9441  False
AsphShn BrkFace   87479.8611  0.769  -60930.6632 235890.3854  False
AsphShn  CBlock       5000.0    0.9 -248557.0644 258557.0644  False
AsphShn CemntBd  114235.3448 0.3588  -33828.9916 262299.6813  False
AsphShn HdBoard   58908.3481    0.9   -87945.424 205762.1202  False
AsphShn ImStucc     162000.0 0.6611  -91557.0644 415557.0644  False
AsphShn MetalSd   50353.8149    0.9  -96511.9516 197219.5815  False
AsphShn Plywood   72811.6603    0.9  -74514.9959 220138.3165  False
AsphShn   Stone     168000.0  0.147  -20990.2774 356990.2774  False
AsphShn  Stucco   61579.6071    0.9  -89949.7216 213108.9359  False
AsphShn VinylSd  108089.8907 0.4427  -38496.4071 254676.1884  False
AsphShn Wd Sdng   47160.9789    0.9  -99743.0159 194064.9738  False
AsphShn WdShing   59834.6486    0.9  -90461.0422 210130.3395  False
BrkComm BrkFace  112813.1944 0.1066   -9179.5051 234805.8939  False
BrkComm  CBlock   30333.3333    0.9 -208722.5596 269389.2262  False
BrkComm CemntBd  139568.6782 0.0086   17997.3709 261139.9854   True
BrkComm HdBoard   84241.6814 0.5247  -35852.3042 204335.6671  False
BrkComm ImStucc  187333.3333 0.3304  -51722.5596 426389.2262  False
BrkComm MetalSd   75687.1483 0.6802  -44421.5042 195795.8007  False
BrkComm Plywood   98144.9936 0.2691   -22526.787 218816.7742  False
BrkComm   Stone  193333.3333 0.0091   24295.2904 362371.3763   True
BrkComm  Stucco   86912.9405 0.5474  -38855.3881 212681.2691  False
BrkComm VinylSd   133423.224 0.0133   13656.4602 253189.9878   True
BrkComm Wd Sdng   72494.3123 0.7387  -47661.0818 192649.7063  False
BrkComm WdShing    85167.982   0.56  -39111.2589 209447.2229  False
BrkFace  CBlock  -82479.8611    0.9 -290941.0774 125981.3552  False
BrkFace CemntBd   26755.4837 0.2734   -6228.4746   59739.442  False
BrkFace HdBoard   -28571.513 0.0265  -55607.1221  -1535.9039   True
BrkFace ImStucc   74520.1389    0.9 -133941.0774 282981.3552  False
BrkFace MetalSd  -37126.0462  0.001  -64226.7317 -10025.3606   True
BrkFace Plywood  -14668.2009    0.9  -44164.5973  14828.1956  False
BrkFace   Stone   80520.1389  0.615  -41472.5606 202512.8384  False
BrkFace  Stucco   -25900.254 0.8295  -72009.1603  20208.6524  False
BrkFace VinylSd   20610.0296  0.282   -4932.8064  46152.8655  False
BrkFace Wd Sdng  -40318.8822  0.001  -67625.9781 -13011.7863   True
BrkFace WdShing  -27645.2125 0.6148  -69522.2763  14231.8514  False
 CBlock CemntBd  109235.3448    0.9  -98979.5507 317450.2403  False
 CBlock HdBoard   53908.3481    0.9  -153447.446 261264.1422  False
 CBlock ImStucc     157000.0  0.885 -135782.4788 449782.4788  False
 CBlock MetalSd   45353.8149    0.9 -162010.4741 252718.1039  False
 CBlock Plywood   67811.6603    0.9 -139879.3087 275502.6293  False
 CBlock   Stone     163000.0 0.5675  -76055.8929 402055.8929  False
 CBlock  Stucco   56579.6071    0.9 -154113.3743 267272.5886  False
 CBlock VinylSd  103089.8907    0.9 -104076.5585 310256.3399  False
 CBlock Wd Sdng   42160.9789    0.9  -165230.387 249552.3449  False
 CBlock WdShing   54834.6486    0.9 -154972.8576 264642.1549  False
CemntBd HdBoard  -55326.9967  0.001  -80392.6738 -30261.3196   True
CemntBd ImStucc   47764.6552    0.9 -160450.2403 255979.5507  False
CemntBd MetalSd  -63881.5299  0.001  -89017.3841 -38745.6757   True
CemntBd Plywood  -41423.6846  0.001  -69125.6955 -13721.6736   True
CemntBd   Stone   53764.6552    0.9  -67806.6521 175335.9624  False
CemntBd  Stucco  -52655.7377 0.0064  -97637.9037  -7673.5717   True
CemntBd VinylSd   -6145.4542    0.9  -29593.2788  17302.3704  False
CemntBd Wd Sdng  -67074.3659  0.001  -92432.6287  -41716.103   True
CemntBd WdShing  -54400.6962  0.001  -95033.8413  -13767.551   True
HdBoard ImStucc  103091.6519    0.9 -104264.1422  310447.446  False
HdBoard MetalSd   -8554.5332    0.9  -25131.4394   8022.3731  False
HdBoard Plywood   13903.3122 0.5577   -6354.6262  34161.2505  False
HdBoard   Stone  109091.6519 0.1245  -11002.3338 229185.6376  False
HdBoard  Stucco     2671.259    0.9  -38150.0333  43492.5514  False
HdBoard VinylSd   49181.5426  0.001   35296.9061  63066.1791   True
HdBoard Wd Sdng  -11747.3692 0.5397  -28659.6176   5164.8793  False
HdBoard WdShing     926.3005    0.9  -35046.3994  36899.0005  False
ImStucc MetalSd -111646.1851 0.8803 -319010.4741  95718.1039  False
ImStucc Plywood  -89188.3397    0.9 -296879.3087 118502.6293  False
ImStucc   Stone       6000.0    0.9 -233055.8929 245055.8929  False
ImStucc  Stucco -100420.3929    0.9 -311113.3743 110272.5886  False
ImStucc VinylSd  -53910.1093    0.9 -261076.5585 153256.3399  False
ImStucc Wd Sdng -114839.0211 0.8469  -322230.387  92552.3449  False
ImStucc WdShing -102165.3514    0.9 -311972.8576 107642.1549  False
MetalSd Plywood   22457.8453 0.0151    2113.1393  42802.5513   True
MetalSd   Stone  117646.1851 0.0623   -2462.4674 237754.8375  False
MetalSd  Stucco   11225.7922    0.9  -29638.6288  52090.2132  False
MetalSd VinylSd   57736.0757  0.001   43725.1468  71747.0046   True
MetalSd Wd Sdng    -3192.836    0.9  -20208.9209  13823.2489  False
MetalSd WdShing    9480.8337    0.9  -26540.8006   45502.468  False
Plywood   Stone   95188.3397 0.3191  -25483.4409 215860.1204  False
Plywood  Stucco  -11232.0531    0.9  -53723.1245  31259.0183  False
Plywood VinylSd   35278.2304  0.001   17060.2071  53496.2537   True
Plywood Wd Sdng  -25650.6813 0.0023  -46269.5414  -5031.8212   True
Plywood WdShing  -12977.0116    0.9  -50833.9579  24879.9347  False
  Stone  Stucco -106420.3929 0.2099 -232188.7215  19347.9358  False
  Stone VinylSd  -59910.1093    0.9 -179676.8731  59856.6544  False
  Stone Wd Sdng -120839.0211 0.0471 -240994.4151    -683.627   True
  Stone WdShing -108165.3514 0.1728 -232444.5922  16113.8895  False
 Stucco VinylSd   46510.2835 0.0067    6661.9433  86358.6237   True
 Stucco Wd Sdng  -14418.6282    0.9  -55420.2286  26582.9722  False
 Stucco WdShing   -1744.9585    0.9  -53601.8985  50111.9815  False
VinylSd Wd Sdng  -60928.9117  0.001  -75335.0374 -46522.7861   True
VinylSd WdShing   -48255.242  0.001  -83119.9441   -13390.54   True
Wd Sdng WdShing   12673.6697    0.9  -23503.5119  48850.8513  False
-------------------------------------------------------------------


data["Exterior1st"].replace('AsphShn', 'AsbShng',inplace = True)
data["Exterior1st"].replace('BrkComm', 'AsbShng',inplace = True)
data["Exterior1st"].replace('CBlock', 'AsbShng',inplace = True)
data["Exterior1st"].replace('ImStucc', 'BrkFace',inplace = True)
data["Exterior1st"].replace('CemntBd', 'VinylSd',inplace = True)
data["Exterior1st"].replace('Stone', 'VinylSd',inplace = True)
data["Exterior1st"].replace('Stucco', 'HdBoard',inplace = True)
data["Exterior1st"].replace('WdShing', 'HdBoard',inplace = True)
data["Exterior1st"].replace('Wd Sdng', 'MetalSd',inplace = True)
data["Exterior1st"].replace('BrkFace', 'Plywood',inplace = True)
data["Exterior1st"].replace('HdBoard', 'MetalSd',inplace = True)


data.Exterior1st.value_counts()

MetalSd    974
VinylSd    840
Plywood    229
AsbShng     30
Name: Exterior1st, dtype: int64


data.Exterior1st.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ Exterior1st', data=data).fit()
print(sm.stats.anova_lm(mod))

                 df        sum_sq       mean_sq           F        PR(>F)
Exterior1st     3.0  1.598341e+12  5.327802e+11  142.963912  3.488991e-84
Residual     2069.0  7.710493e+12  3.726676e+09         NaN           NaN


t_Exterior1st = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior1st)
print(t_Exterior1st)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
 group1  group2   meandiff  p-adj   lower       upper    reject
---------------------------------------------------------------
AsbShng MetalSd  52300.0598 0.001 23205.5802  81394.5394   True
AsbShng Plywood  77235.1984 0.001 46759.3814 107711.0153   True
AsbShng VinylSd 108362.5821 0.001 79198.8434 137526.3209   True
MetalSd Plywood  24935.1386 0.001 13408.0551   36462.222   True
MetalSd VinylSd  56062.5223 0.001   48671.86  63453.1847   True
Plywood VinylSd  31127.3837 0.001 19426.5903  42828.1771   True
---------------------------------------------------------------


sns.countplot(data.Exterior1st)
plt.title('Count plot of Exterior1st')

Text(0.5, 1.0, 'Count plot of Exterior1st')


sns.catplot('Exterior1st', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Property_Sale_Price vs Exterior1st')

Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs Exterior1st')


sns.boxplot('Exterior1st', 'Property_Sale_Price', data=data)
plt.title('Box plot of Property_Sale_Price vs Exterior1st')

Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs Exterior1st')


data.Exterior2nd.isnull().sum()

0


data.Exterior2nd.value_counts()

VinylSd    729
MetalSd    299
HdBoard    292
Wd Sdng    277
Plywood    205
CmentBd     85
Wd Shng     55
BrkFace     35
Stucco      29
AsbShng     27
ImStucc     14
Brk Cmn     11
Stone        9
AsphShn      4
CBlock       1
Other        1
Name: Exterior2nd, dtype: int64


data.Exterior2nd.value_counts().sum()

2073


sns.countplot(data.Exterior2nd)
plt.title('Count plot of Exterior2nd')

Text(0.5, 1.0, 'Count plot of Exterior2nd')


data.groupby("Exterior2nd")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Exterior2nd', data=data).fit()
print(sm.stats.anova_lm(mod))

                 df        sum_sq       mean_sq          F        PR(>F)
Exterior2nd    15.0  1.648526e+12  1.099017e+11  29.511593  2.410342e-76
Residual     2057.0  7.660308e+12  3.724019e+09        NaN           NaN


t_Exterior2nd = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior2nd)
print(t_Exterior2nd)

        Multiple Comparison of Means - Tukey HSD, FWER=0.05        
===================================================================
 group1  group2   meandiff   p-adj     lower        upper    reject
-------------------------------------------------------------------
AsbShng AsphShn    17783.963    0.9  -94374.4658 129942.3917  False
AsbShng Brk Cmn   19647.5993    0.9   -55234.243  94529.4417  False
AsbShng BrkFace    82753.963  0.001   29131.9778 136375.9481   True
AsbShng  CBlock    -5716.037    0.9 -218902.7242 207470.6502  False
AsbShng CmentBd  102583.6688  0.001   56336.9832 148830.3545   True
AsbShng HdBoard   54826.5589  0.001   12716.5841  96936.5336   True
AsbShng ImStucc  119976.8201  0.001   51030.8359 188922.8044   True
AsbShng MetalSd    40218.662 0.0799   -1849.5711   82286.895  False
AsbShng   Other   208283.963 0.0642   -4902.7242 421470.6502  False
AsbShng Plywood   54667.1386 0.0014   11807.5372  97526.7399   True
AsbShng   Stone   17575.5185    0.9  -63001.4754  98152.5124  False
AsbShng  Stucco   45594.7561 0.2737  -10390.8108 101580.3229  False
AsbShng VinylSd    97972.487  0.001   56944.6899  139000.284   True
AsbShng Wd Sdng   35314.4864 0.2313   -6891.8807  77520.8536  False
AsbShng Wd Shng   53155.7811 0.0197     3962.403 102349.1593   True
AsphShn Brk Cmn    1863.6364    0.9 -120367.6096 124094.8823  False
AsphShn BrkFace      64970.0 0.7882  -45522.1013 175462.1013  False
AsphShn  CBlock     -23500.0    0.9 -257555.0165 210555.0165  False
AsphShn CmentBd   84799.7059  0.321  -22307.4512  191906.863  False
AsphShn HdBoard   37042.5959    0.9  -68344.4865 142429.6783  False
AsphShn ImStucc  102192.8571 0.1899  -16494.6987  220880.413  False
AsphShn MetalSd    22434.699    0.9  -82935.7115 127805.1094  False
AsphShn   Other     190500.0 0.2747  -43555.0165 424555.0165  False
AsphShn Plywood   36883.1756    0.9  -68805.6724 142572.0236  False
AsphShn   Stone    -208.4444    0.9 -126009.2358 125592.3469  False
AsphShn  Stucco   27810.7931    0.9  -83847.4841 139469.0703  False
AsphShn VinylSd    80188.524 0.3879  -24770.8362 185147.8842  False
AsphShn Wd Sdng   17530.5235    0.9  -87895.1119 122956.1588  False
AsphShn Wd Shng   35371.8182    0.9  -73040.2458 143783.8822  False
Brk Cmn BrkFace   63106.3636 0.1737   -9255.7924 135468.5197  False
Brk Cmn  CBlock  -25363.6364    0.9 -244017.5363 193290.2635  False
Brk Cmn CmentBd   82936.0695 0.0024   15856.1152 150016.0239   True
Brk Cmn HdBoard   35178.9595 0.8789  -29118.8967  99476.8158  False
Brk Cmn ImStucc  100329.2208 0.0048   15981.6477 184676.7939   True
Brk Cmn MetalSd   20571.0626    0.9  -43699.4638  84841.5891  False
Brk Cmn   Other  188636.3636 0.1872  -30017.5363 407290.2635  False
Brk Cmn Plywood   35019.5392 0.8935  -29771.7393  99810.8178  False
Brk Cmn   Stone   -2072.0808    0.9  -96165.7378  92021.5762  False
Brk Cmn  Stucco   25947.1567    0.9   -48183.458 100077.7715  False
Brk Cmn VinylSd   78324.8876 0.0026   14730.5131 141919.2622   True
Brk Cmn Wd Sdng   15666.8871    0.9  -48694.1397  80027.9139  False
Brk Cmn Wd Shng   33508.1818    0.9  -35636.2525 102652.6161  False
BrkFace  CBlock     -88470.0    0.9 -300784.7543 123844.7543  False
BrkFace CmentBd   19829.7059    0.9  -22214.8806  61874.2924  False
BrkFace HdBoard  -27927.4041 0.4337  -65373.9165   9519.1083  False
BrkFace ImStucc   37222.8571 0.8453  -28977.8986 103423.6129  False
BrkFace MetalSd   -42535.301 0.0096  -79934.8672  -5135.7348   True
BrkFace   Other     125530.0 0.7811  -86784.7543 337844.7543  False
BrkFace Plywood  -28086.8244 0.4638  -66374.3778   10200.729  False
BrkFace   Stone  -65178.4444 0.2375 -143419.3753  13062.4864  False
BrkFace  Stucco  -37159.2069 0.5247  -89727.0204  15408.6066  False
BrkFace VinylSd    15218.524    0.9  -21006.7634  51443.8115  False
BrkFace Wd Sdng  -47439.4765 0.0017   -84994.353  -9884.6001   True
BrkFace Wd Shng  -29598.1818 0.6422  -74863.8395  15667.4759  False
 CBlock CmentBd  108299.7059    0.9 -102273.3065 318872.7183  False
 CBlock HdBoard   60542.5959    0.9 -149160.7365 270245.9282  False
 CBlock ImStucc  125692.8571 0.8059  -90999.9816 342385.6959  False
 CBlock MetalSd    45934.699    0.9 -163760.2553 255629.6533  False
 CBlock   Other     214000.0 0.4891    -82058.78   510058.78  False
 CBlock Plywood   60383.1756    0.9 -149471.9722 270238.3234  False
 CBlock   Stone   23291.5556    0.9 -197377.6303 243960.7414  False
 CBlock  Stucco   51310.7931    0.9  -161613.187 264234.7732  False
 CBlock VinylSd   103688.524    0.9 -105800.1816 313177.2296  False
 CBlock Wd Sdng   41030.5235    0.9 -168692.1864 250753.2334  False
 CBlock Wd Shng   58871.8182    0.9 -152367.9178 270111.5542  False
CmentBd HdBoard    -47757.11  0.001  -73557.8839  -21956.336   True
CmentBd ImStucc   17393.1513    0.9   -42988.789  77775.0915  False
CmentBd MetalSd  -62365.0069  0.001   -88097.597 -36632.4168   True
CmentBd   Other  105700.2941    0.9 -104872.7183 316273.3065  False
CmentBd Plywood  -47916.5303  0.001  -74923.4811 -20909.5795   True
CmentBd   Stone  -85008.1503 0.0073 -158391.2676  -11625.033   True
CmentBd  Stucco  -56988.9128 0.0016   -102009.06 -11968.7655   True
CmentBd VinylSd   -4611.1819    0.9  -28605.1414  19382.7777  False
CmentBd Wd Sdng  -67269.1824  0.001  -93226.9826 -41311.3822   True
CmentBd Wd Shng  -49427.8877  0.001  -85655.2036 -13200.5718   True
HdBoard ImStucc   65150.2613 0.0096    7874.8499 122425.6726   True
HdBoard MetalSd  -14607.8969 0.2112  -31831.7244   2615.9306  False
HdBoard   Other  153457.4041 0.4681  -56245.9282 363160.7365  False
HdBoard Plywood    -159.4203    0.9  -19234.7821  18915.9416  False
HdBoard   Stone  -37251.0403    0.9 -108100.0061  33597.9254  False
HdBoard  Stucco   -9231.8028    0.9  -49990.9453  31527.3397  False
HdBoard VinylSd   43145.9281  0.001   28647.5097  57644.3465   True
HdBoard Wd Sdng  -19512.0724 0.0135  -37070.5869  -1953.5579   True
HdBoard Wd Shng   -1670.7777    0.9  -32442.7204  29101.1649  False
ImStucc MetalSd  -79758.1581  0.001 -137002.8872 -22513.4291   True
ImStucc   Other   88307.1429    0.9 -128385.6959 304999.9816  False
ImStucc Plywood  -65309.6815 0.0107  -123138.465   -7480.898   True
ImStucc   Stone -102401.3016 0.0087 -191843.3203 -12959.2829   True
ImStucc  Stucco   -74382.064 0.0172 -142511.4004  -6252.7277   True
ImStucc VinylSd  -22004.3331    0.9    -78488.87  34480.2037  False
ImStucc Wd Sdng  -84662.3337  0.001 -142008.6518 -27316.0156   True
ImStucc Wd Shng   -66821.039 0.0234 -129488.5044  -4153.5735   True
MetalSd   Other   168065.301 0.3004  -41629.6533 377760.2553  False
MetalSd Plywood   14448.4766 0.3951   -4534.5602  33431.5135  False
MetalSd   Stone  -22643.1434    0.9  -93467.3074  48181.0205  False
MetalSd  Stucco    5376.0941    0.9  -35339.9219  46092.1101  False
MetalSd VinylSd    57753.825  0.001    43377.094   72130.556   True
MetalSd Wd Sdng   -4904.1755    0.9  -22362.3452  12553.9942  False
MetalSd Wd Shng   12937.1192    0.9  -17777.6771  43651.9155  False
  Other Plywood -153616.8244 0.4675 -363471.9722  56238.3234  False
  Other   Stone -190708.4444 0.1848 -411377.6303  29960.7414  False
  Other  Stucco -162689.2069 0.3877  -375613.187  50234.7732  False
  Other VinylSd  -110311.476    0.9 -319800.1816  99177.2296  False
  Other Wd Sdng -172969.4765 0.2532 -382692.1864  36753.2334  False
  Other Wd Shng -155128.1818 0.4618 -366367.9178  56111.5542  False
Plywood   Stone  -37091.6201    0.9 -108388.6845  34205.4444  False
Plywood  Stucco   -9072.3825    0.9  -50605.5398  32460.7748  False
Plywood VinylSd   43305.3484  0.001   26755.4363  59855.2605   True
Plywood Wd Sdng  -19352.6521 0.0482  -38639.8729    -65.4314   True
Plywood Wd Shng   -1511.3574    0.9  -33301.4173  30278.7025  False
  Stone  Stucco   28019.2375    0.9  -51860.1069  107898.582  False
  Stone VinylSd   80396.9684 0.0087   10185.8147 150608.1222   True
  Stone Wd Sdng   17738.9679    0.9  -53167.3322   88645.268  False
  Stone Wd Shng   35580.2626    0.9   -39694.663 110855.1882  False
 Stucco VinylSd   52377.7309  0.001   12737.6267  92017.8351   True
 Stucco Wd Sdng  -10280.2696    0.9  -51138.9915  30578.4522  False
 Stucco Wd Shng    7561.0251    0.9  -40481.1041  55603.1543  False
VinylSd Wd Sdng  -62658.0005  0.001  -77434.0484 -47881.9527   True
VinylSd Wd Shng  -44816.7058  0.001   -74090.288 -15543.1237   True
Wd Sdng Wd Shng   17841.2947 0.8119  -13062.4253  48745.0147  False
-------------------------------------------------------------------


data["Exterior2nd"].replace('AsphShn', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('Brk Cmn', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('CBlock', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('Stone', 'AsbShng',inplace = True)
data["Exterior2nd"].replace('BrkFace', 'VinylSd',inplace = True)
data["Exterior2nd"].replace('CmentBd', 'VinylSd',inplace = True)
data["Exterior2nd"].replace('Plywood', 'HdBoard',inplace = True)
data["Exterior2nd"].replace('Stucco', 'HdBoard',inplace = True)
data["Exterior2nd"].replace('Wd Shng', 'HdBoard',inplace = True)
data["Exterior2nd"].replace('Wd Sdng', 'MetalSd',inplace = True)
data["Exterior2nd"].replace('ImStucc', 'VinylSd',inplace = True)
data["Exterior2nd"].replace('Other', 'VinylSd',inplace = True)


data.Exterior2nd.value_counts()

VinylSd    864
HdBoard    581
MetalSd    576
AsbShng     52
Name: Exterior2nd, dtype: int64


data.Exterior2nd.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ Exterior2nd', data=data).fit()
print(sm.stats.anova_lm(mod))

                 df        sum_sq       mean_sq           F        PR(>F)
Exterior2nd     3.0  1.609325e+12  5.364417e+11  144.151782  8.010779e-85
Residual     2069.0  7.699509e+12  3.721367e+09         NaN           NaN


t_Exterior2nd = pairwise_tukeyhsd(data.Property_Sale_Price, data.Exterior2nd)
print(t_Exterior2nd)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
 group1  group2   meandiff  p-adj     lower       upper    reject
-----------------------------------------------------------------
AsbShng HdBoard  45695.1373  0.001  22991.9732  68398.3014   True
AsbShng MetalSd  29404.0174 0.0049     6692.76  52115.2748   True
AsbShng VinylSd  89837.6562  0.001  67442.0169 112233.2956   True
HdBoard MetalSd -16291.1199  0.001 -25513.4708   -7068.769   True
HdBoard VinylSd   44142.519  0.001  35727.3419   52557.696   True
MetalSd VinylSd  60433.6389  0.001  51996.6514  68870.6264   True
-----------------------------------------------------------------


sns.countplot(data.Exterior2nd)
plt.title('Count plot of Exterior2nd')

Text(0.5, 1.0, 'Count plot of Exterior2nd')


sns.catplot('Exterior2nd', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Property_Sale_Price vs Exterior2nd')

Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs Exterior2nd')


sns.boxplot('Exterior2nd', 'Property_Sale_Price', data=data)
plt.title('Box plot of Property_Sale_Price vs Exterior2nd')

Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs Exterior2nd')


data.MasVnrType.isnull().sum()

14


data.MasVnrType.dtype

dtype('O')


data.MasVnrType.mode()

0    None
dtype: object


data.MasVnrType.fillna(data.MasVnrType.mode()[0] , inplace = True)


data.MasVnrType.isnull().sum()

0


data.MasVnrType.value_counts()

None       1247
BrkFace     620
Stone       186
BrkCmn       20
Name: MasVnrType, dtype: int64


data.MasVnrType.value_counts().sum()

2073


sns.countplot(data.MasVnrType)
plt.title('Count plot of MasVnrType')

Text(0.5, 1.0, 'Count plot of MasVnrType')


data.groupby("MasVnrType")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ MasVnrType', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F        PR(>F)
MasVnrType     3.0  1.826592e+12  6.088639e+11  168.363874  1.177350e-97
Residual    2069.0  7.482243e+12  3.616357e+09         NaN           NaN


t_MasVnrType = pairwise_tukeyhsd(data.Property_Sale_Price, data.MasVnrType)
print(t_MasVnrType)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
 group1  group2   meandiff  p-adj     lower       upper    reject
-----------------------------------------------------------------
 BrkCmn BrkFace  55317.3677  0.001  20190.6321  90444.1034   True
 BrkCmn    None  11399.6216 0.8131 -23450.0532  46249.2965  False
 BrkCmn   Stone 103116.6005  0.001  66731.7339 139501.4672   True
BrkFace    None -43917.7461  0.001 -51515.7889 -36319.7033   True
BrkFace   Stone  47799.2328  0.001  34872.9441  60725.5215   True
   None   Stone  91716.9789  0.001  79563.7398 103870.2181   True
-----------------------------------------------------------------


data["MasVnrType"].replace('BrkCmn', 'None',inplace = True)


data.MasVnrType.value_counts()

None       1267
BrkFace     620
Stone       186
Name: MasVnrType, dtype: int64


data.MasVnrType.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ MasVnrType', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F        PR(>F)
MasVnrType     2.0  1.824034e+12  9.120168e+11  252.227801  9.330649e-99
Residual    2070.0  7.484801e+12  3.615846e+09         NaN           NaN


t_MasVnrType = pairwise_tukeyhsd(data.Property_Sale_Price, data.MasVnrType)
print(t_MasVnrType)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
 group1 group2   meandiff  p-adj    lower       upper    reject
---------------------------------------------------------------
BrkFace   None -44097.6928 0.001 -51010.0561 -37185.3295   True
BrkFace  Stone  47799.2328 0.001  36008.5228  59589.9428   True
   None  Stone  91896.9256 0.001  80822.7149 102971.1363   True
---------------------------------------------------------------


sns.countplot(data.MasVnrType)
plt.title('Count plot of MasVnrType')

Text(0.5, 1.0, 'Count plot of MasVnrType')


sns.catplot('MasVnrType', 'Property_Sale_Price' , data=data)
plt.title('Cat plot of Property_Sale_Price vs MasVnrType')

Text(0.5, 1.0, 'Cat plot of Property_Sale_Price vs MasVnrType')


sns.boxplot('MasVnrType', 'Property_Sale_Price' , data=data)
plt.title('Box plot of Property_Sale_Price vs MasVnrType')

Text(0.5, 1.0, 'Box plot of Property_Sale_Price vs MasVnrType')


data.MasVnrArea.isnull().sum()

14


data.MasVnrArea.dtype

dtype('float64')


data.MasVnrArea.describe()

count    2059.000000
mean      101.875668
std       179.169217
min         0.000000
25%         0.000000
50%         0.000000
75%       161.500000
max      1600.000000
Name: MasVnrArea, dtype: float64


data.MasVnrArea.sample(10)

875        0.0
36         0.0
628      192.0
847        0.0
115     1129.0
820        0.0
1026     324.0
1299       0.0
534        0.0
679       61.0
Name: MasVnrArea, dtype: float64


data.MasVnrArea.fillna(data.MasVnrArea.median(), inplace = True)


data.MasVnrArea.isnull().sum()

0


data.MasVnrArea.describe()

count    2073.000000
mean      101.187651
std       178.757820
min         0.000000
25%         0.000000
50%         0.000000
75%       160.000000
max      1600.000000
Name: MasVnrArea, dtype: float64


data.MasVnrArea.value_counts()

0.0      1243
108.0      12
340.0      10
210.0       9
106.0       9
         ... 
299.0       1
97.0        1
278.0       1
292.0       1
428.0       1
Name: MasVnrArea, Length: 342, dtype: int64


data.MasVnrArea.value_counts().sum()

2073


plt.hist(data.MasVnrArea)
plt.title('Hist of MasVnrArea')

Text(0.5, 1.0, 'Hist of MasVnrArea')


sns.distplot(data.MasVnrArea)
plt.title('Normality MasVnrArea')

Text(0.5, 1.0, 'Normality MasVnrArea')


sns.countplot(data.MasVnrArea)
plt.title('count plot of MasVnrArea')

Text(0.5, 1.0, 'count plot of MasVnrArea')


sns.boxplot(x = data.MasVnrArea, data =data, color = 'r')
plt.title('Box plot of MasVnrArea')

Text(0.5, 1.0, 'Box plot of MasVnrArea')


IQR = data.MasVnrArea.quantile(0.75) - data.MasVnrArea.quantile(0.25)
IQR

160.0


upper_limit = data.MasVnrArea.quantile(0.75) + (1.5*IQR)
upper_limit

400.0


lower_limit = data.MasVnrArea.quantile(0.25) - (1.5*IQR)
lower_limit

-240.0


len(data.MasVnrArea[data.MasVnrArea > upper_limit])

143


len(data.MasVnrArea[data.MasVnrArea < lower_limit])

0


for i in np.arange(upper_limit, 800, 200):
    outliers = len(data.MasVnrArea[data.MasVnrArea > i])
    print('At a limit of: ', i, 'There are ', outliers)

At a limit of:  400.0 There are  143
At a limit of:  600.0 There are  54


data['MasVnrArea'] = np.where(data['MasVnrArea'] > upper_limit, upper_limit, data['MasVnrArea'])


sns.boxplot(x = data.MasVnrArea, data =data, color = 'g')
plt.title('Box plot of MasVnrArea')

Text(0.5, 1.0, 'Box plot of MasVnrArea')


sns.distplot(data.MasVnrArea)
plt.title('Normality of MasVnrArea')

Text(0.5, 1.0, 'Normality of MasVnrArea')


np.corrcoef(data.MasVnrArea, data.Property_Sale_Price)

array([[1.        , 0.45503223],
       [0.45503223, 1.        ]])


sns.scatterplot(x = data['MasVnrArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('MasVnrArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of MasVnrArea vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter plot of MasVnrArea vs Property_Sale_Price')


data.ExterQual.isnull().sum()

0


data.ExterQual.value_counts()

TA    1272
Gd     706
Ex      76
Fa      19
Name: ExterQual, dtype: int64


data.ExterQual.value_counts().sum()

2073


sns.countplot(data.ExterQual)
plt.title('Count plot of ExterQual')

Text(0.5, 1.0, 'Count plot of ExterQual')


data.groupby("ExterQual")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ ExterQual', data=data).fit()
print(sm.stats.anova_lm(mod))

               df        sum_sq       mean_sq           F         PR(>F)
ExterQual     3.0  4.600500e+12  1.533500e+12  673.871283  1.466181e-305
Residual   2069.0  4.708334e+12  2.275657e+09         NaN            NaN


t_ExterQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.ExterQual)
print(t_ExterQual)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj    lower        upper     reject
-----------------------------------------------------------------
    Ex     Fa -213256.8026 0.001 -244716.5052 -181797.1001   True
    Ex     Gd  -76580.1942 0.001  -91387.3155  -61773.0728   True
    Ex     TA  -160633.465 0.001 -175116.8808 -146150.0491   True
    Fa     Gd  136676.6085 0.001  108162.0758  165191.1412   True
    Fa     TA   52623.3376 0.001     24275.55   80971.1253   True
    Gd     TA  -84053.2708 0.001  -89809.5738  -78296.9679   True
-----------------------------------------------------------------


sns.catplot('ExterQual', 'Property_Sale_Price', data=data)
plt.title('Cat plot of ExterQual vs Property_sale_price')

Text(0.5, 1.0, 'Cat plot of ExterQual vs Property_sale_price')


sns.boxplot('ExterQual', 'Property_Sale_Price', data=data)
plt.title('Box plot of ExterQual vs Property_sale_price')

Text(0.5, 1.0, 'Box plot of ExterQual vs Property_sale_price')


data.ExterCond.isnull().sum()

0


data.ExterCond.value_counts()

TA    1815
Gd     219
Fa      35
Ex       3
Po       1
Name: ExterCond, dtype: int64


data.ExterCond.value_counts().sum()

2073


sns.countplot(data.ExterCond)
plt.title('Count plot of ExterCond')

Text(0.5, 1.0, 'Count plot of ExterCond')


data.groupby("ExterCond")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ ExterCond', data=data).fit()
print(sm.stats.anova_lm(mod))

               df        sum_sq       mean_sq          F        PR(>F)
ExterCond     4.0  2.073636e+11  5.184089e+10  11.779082  1.838421e-09
Residual   2068.0  9.101471e+12  4.401098e+09        NaN           NaN


t_ExterCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.ExterCond)
print(t_ExterCond)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
    Ex     Fa   -94386.819 0.1251  -203346.949  14573.3109  False
    Ex     Gd  -31001.3014    0.9 -136285.7595  74283.1568  False
    Ex     Po -124833.3333 0.4793 -333974.6473  84307.9806  False
    Ex     TA  -21485.9201    0.9 -126142.9634  83171.1232  False
    Fa     Gd   63385.5177  0.001   30414.5842  96356.4511   True
    Fa     Po  -30446.5143    0.9 -214137.4355 153244.4069  False
    Fa     TA   72900.8989  0.001   41991.9675 103809.8304   True
    Gd     Po   -93832.032 0.6046 -275366.7717  87702.7078  False
    Gd     TA    9515.3813 0.2638   -3441.0551  22471.8176  False
    Po     TA  103347.4132 0.5216  -77824.1666  284518.993  False
-----------------------------------------------------------------


data["ExterCond"].replace('Ex', 'TA',inplace = True)
data["ExterCond"].replace('Gd', 'TA',inplace = True)
data["ExterCond"].replace('Po', 'Fa',inplace = True)


data.ExterCond.value_counts()

TA    2037
Fa      36
Name: ExterCond, dtype: int64


data.ExterCond.value_counts().sum()

2073


df1=data[data.ExterCond=='TA']

df2=data[data.ExterCond=='Fa']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=6.520283823345744, pvalue=8.790776565581211e-11)


sns.countplot(data.ExterCond)
plt.title('Count plot of ExterCond')

Text(0.5, 1.0, 'Count plot of ExterCond')


sns.catplot('ExterCond', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of ExterCond vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of ExterCond vs Property_Sale_Price')


sns.boxplot('ExterCond', 'Property_Sale_Price', data=data)
plt.title('Box Plot of ExterCond vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of ExterCond vs Property_Sale_Price')


data.Foundation.isnull().sum()

0


data.Foundation.value_counts()

PConc     928
CBlock    901
BrkTil    191
Slab       40
Stone      10
Wood        3
Name: Foundation, dtype: int64


data.Foundation.value_counts().sum()

2073


sns.countplot(data.Foundation)
plt.title('Count plot of Foundation')

Text(0.5, 1.0, 'Count plot of Foundation')


data.groupby("Foundation")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Foundation', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F         PR(>F)
Foundation     5.0  2.824406e+12  5.648812e+11  180.063606  2.183733e-159
Residual    2067.0  6.484428e+12  3.137120e+09         NaN            NaN


t_Foundation = pairwise_tukeyhsd(data.Property_Sale_Price, data.Foundation)
print(t_Foundation)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
BrkTil CBlock   19622.4127  0.001    6896.1395  32348.6859   True
BrkTil  PConc    87903.445  0.001   75209.5946 100597.2953   True
BrkTil   Slab  -18929.7682 0.3769  -46709.5169   8849.9805  False
BrkTil  Stone   61372.1068 0.0097    9545.7898 113198.4238   True
BrkTil   Wood   55965.3735 0.5173  -36993.8648 148924.6117  False
CBlock  PConc   68281.0323  0.001   60808.9874  75753.0771   True
CBlock   Slab  -38552.1809  0.001  -64367.1389 -12737.2229   True
CBlock  Stone   41749.6941 0.1768   -9050.5495  92549.9378  False
CBlock   Wood   36342.9608 0.8608  -56048.1513 128734.0729  False
 PConc   Slab -106833.2131  0.001 -132632.2027 -81034.2236   True
 PConc  Stone  -26531.3381 0.6485  -77323.4691  24260.7928  False
 PConc   Wood  -31938.0715    0.9 -124324.7231  60448.5802  False
  Slab  Stone    80301.875  0.001   23818.0617 136785.6883   True
  Slab   Wood   74895.1417 0.2227  -20738.9213 170529.2047  False
 Stone   Wood   -5406.7333    0.9 -110573.8703  99760.4037  False
-----------------------------------------------------------------


data["Foundation"].replace('Wood', 'PConc',inplace = True)
data["Foundation"].replace('Stone', 'PConc',inplace = True)
data["Foundation"].replace('Slab', 'BrkTil',inplace = True)


data.Foundation.value_counts()

PConc     941
CBlock    901
BrkTil    231
Name: Foundation, dtype: int64


data.Foundation.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ Foundation', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F         PR(>F)
Foundation     2.0  2.802594e+12  1.401297e+12  445.831193  9.772675e-162
Residual    2070.0  6.506240e+12  3.143111e+09         NaN            NaN


t_Foundation = pairwise_tukeyhsd(data.Property_Sale_Price, data.Foundation)
print(t_Foundation)

    Multiple Comparison of Means - Tukey HSD, FWER=0.05     
============================================================
group1 group2  meandiff  p-adj   lower       upper    reject
------------------------------------------------------------
BrkTil CBlock 22900.2946 0.001 13202.9125  32597.6768   True
BrkTil  PConc 90797.5569 0.001 81142.3254 100452.7883   True
CBlock  PConc 67897.2622 0.001 61768.2952  74026.2293   True
------------------------------------------------------------


sns.countplot(data.Foundation)
plt.title('Count plot of Foundation')

Text(0.5, 1.0, 'Count plot of Foundation')


sns.catplot('Foundation', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Foundation vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of Foundation vs Property_Sale_Price')


sns.boxplot('Foundation', 'Property_Sale_Price', data=data)
plt.title('Box plot of Foundation vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of Foundation vs Property_Sale_Price')


data.BsmtQual.isnull().sum()

59


data.BsmtQual.dtype

dtype('O')


data['BsmtQual'] = data["BsmtQual"].fillna("No Basement")


data.BsmtQual.isnull().sum()

0


data.BsmtQual.value_counts()

Gd             904
TA             891
Ex             169
No Basement     59
Fa              50
Name: BsmtQual, dtype: int64


data.BsmtQual.value_counts().sum()

2073


sns.countplot(data.BsmtQual)
plt.title('Count plot of BsmtQual')

Text(0.5, 1.0, 'Count plot of BsmtQual')


data.groupby("BsmtQual")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BsmtQual', data=data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq           F         PR(>F)
BsmtQual     4.0  4.405123e+12  1.101281e+12  464.433686  7.169002e-286
Residual  2068.0  4.903711e+12  2.371234e+09         NaN            NaN


t_B = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtQual, alpha = 0.05)
print(t_B)

            Multiple Comparison of Means - Tukey HSD, FWER=0.05             
============================================================================
   group1      group2     meandiff   p-adj     lower        upper     reject
----------------------------------------------------------------------------
         Ex          Fa  -170723.225  0.001 -192126.0361 -149320.4138   True
         Ex          Gd  -87946.9226  0.001    -99088.57  -76805.2752   True
         Ex No Basement -182790.6958  0.001 -202894.3582 -162687.0334   True
         Ex          TA -150111.4935  0.001 -161265.9353 -138957.0516   True
         Fa          Gd   82776.3023  0.001   63461.8638  102090.7408   True
         Fa No Basement  -12067.4708 0.6737  -37622.6752   13487.7335  False
         Fa          TA   20611.7315 0.0298    1289.9096   39933.5534   True
         Gd No Basement  -94843.7732  0.001 -112707.8279  -76979.7185   True
         Gd          TA  -62164.5708  0.001  -68440.6191  -55888.5225   True
No Basement          TA   32679.2024  0.001   14807.1651   50551.2396   True
----------------------------------------------------------------------------


data["BsmtQual"].replace('Fa', 'No Basement',inplace = True)


data.BsmtQual.value_counts()

Gd             904
TA             891
Ex             169
No Basement    109
Name: BsmtQual, dtype: int64


data.BsmtQual.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ BsmtQual', data=data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq           F         PR(>F)
BsmtQual     3.0  4.401182e+12  1.467061e+12  618.492965  6.085538e-287
Residual  2069.0  4.907652e+12  2.371992e+09         NaN            NaN


t_B = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtQual, alpha = 0.05)
print(t_B)

            Multiple Comparison of Means - Tukey HSD, FWER=0.05            
===========================================================================
   group1      group2     meandiff   p-adj    lower        upper     reject
---------------------------------------------------------------------------
         Ex          Gd  -87946.9226 0.001  -98441.1856  -77452.6597   True
         Ex No Basement -177255.1587 0.001 -192638.3148 -161872.0027   True
         Ex          TA -150111.4935 0.001 -160617.8075 -139605.1794   True
         Gd No Basement  -89308.2361 0.001 -102004.8206  -76611.6516   True
         Gd          TA  -62164.5708 0.001  -68075.9499  -56253.1918   True
No Basement          TA   27143.6653 0.001   14437.1182   39850.2123   True
---------------------------------------------------------------------------


sns.countplot(data.BsmtQual)
plt.title('Count plot of BsmtQual')

Text(0.5, 1.0, 'Count plot of BsmtQual')


sns.catplot('BsmtQual', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtQual vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of BsmtQual vs Property_Sale_Price')


sns.boxplot('BsmtQual', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtQual vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of BsmtQual vs Property_Sale_Price')


data.BsmtCond.isnull().sum()

59


data.BsmtCond.dtype

dtype('O')


data.BsmtCond.fillna('No basement', inplace = True)


data.BsmtCond.isnull().sum()

0


data.BsmtCond.value_counts()

TA             1851
Gd              100
Fa               61
No basement      59
Po                2
Name: BsmtCond, dtype: int64


data.BsmtCond.value_counts().sum()

2073


sns.countplot(data.BsmtCond)
plt.title('Count plot of BsmtCond')

Text(0.5, 1.0, 'Count plot of BsmtCond')


data.groupby("BsmtCond")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BsmtCond', data = data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq          F        PR(>F)
BsmtCond     4.0  6.233294e+11  1.558324e+11  37.103348  5.283618e-30
Residual  2068.0  8.685505e+12  4.199954e+09        NaN           NaN


t_BsmtCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtCond)
print(t_BsmtCond)

            Multiple Comparison of Means - Tukey HSD, FWER=0.05            
===========================================================================
   group1      group2     meandiff   p-adj     lower        upper    reject
---------------------------------------------------------------------------
         Fa          Gd   88056.9348  0.001   59312.0726 116801.7969   True
         Fa No basement  -18770.9361 0.5053  -51079.0726  13537.2004  False
         Fa          Po  -61764.8852 0.6527 -188910.8464   65381.076  False
         Fa          TA   53951.4384  0.001    30927.054  76975.8227   True
         Gd No basement -106827.8708  0.001 -135873.7661 -77781.9756   True
         Gd          Po   -149821.82 0.0108 -276178.2357 -23465.4043   True
         Gd          TA  -34105.4964  0.001  -52270.5898  -15940.403   True
No basement          Po  -42993.9492 0.8816 -170208.3052  84220.4069  False
No basement          TA   72722.3745  0.001   49323.2462  96121.5027   True
         Po          TA  115716.3236 0.0859    -9462.744 240895.3912  False
---------------------------------------------------------------------------


data.BsmtCond.replace('Po', 'No basement', inplace = True)
data.BsmtCond.replace('Fa', 'No basement', inplace = True)


data.BsmtCond.value_counts()

TA             1851
No basement     122
Gd              100
Name: BsmtCond, dtype: int64


data.BsmtCond.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ BsmtCond', data = data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq         F        PR(>F)
BsmtCond     2.0  6.073324e+11  3.036662e+11  72.23914  4.713926e-31
Residual  2070.0  8.701502e+12  4.203624e+09       NaN           NaN


t_BsmtCond = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtCond)
print(t_BsmtCond)

           Multiple Comparison of Means - Tukey HSD, FWER=0.05           
=========================================================================
   group1      group2     meandiff  p-adj    lower        upper    reject
-------------------------------------------------------------------------
         Gd No basement -98147.2216 0.001 -118660.1809 -77634.2624   True
         Gd          TA -34105.4964 0.001  -49717.4471 -18493.5457   True
No basement          TA  64041.7252 0.001    49827.861  78255.5895   True
-------------------------------------------------------------------------


sns.countplot(data.BsmtCond)
plt.title('Count plot of BsmtCond')

Text(0.5, 1.0, 'Count plot of BsmtCond')


sns.catplot('BsmtCond', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtCond vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of BsmtCond vs Property_Sale_Price')


sns.boxplot('BsmtCond', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtCond vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of BsmtCond vs Property_Sale_Price')


data.BsmtExposure.isnull().sum()

61


data.BsmtExposure.dtype

dtype('O')


data.BsmtExposure.fillna('No Basement', inplace = True)


data.BsmtExposure.isnull().sum()

0


data.BsmtExposure.value_counts()

No             1349
Av              322
Gd              181
Mn              160
No Basement      61
Name: BsmtExposure, dtype: int64


data.BsmtExposure.value_counts().sum()

2073


sns.countplot(data.BsmtExposure)
plt.title('Count plot of BsmtExposure')

Text(0.5, 1.0, 'Count plot of BsmtExposure')


data.groupby("BsmtExposure")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BsmtExposure', data = data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq          F        PR(>F)
BsmtExposure     4.0  1.250910e+12  3.127276e+11  80.258963  2.202829e-63
Residual      2068.0  8.057924e+12  3.896482e+09        NaN           NaN


t_BsmtExposure = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtExposure)
print(t_BsmtExposure)

         Multiple Comparison of Means - Tukey HSD, FWER=0.05         
=====================================================================
group1    group2     meandiff   p-adj    lower        upper    reject
---------------------------------------------------------------------
    Av          Gd    35546.243 0.001   19713.9788  51378.5072   True
    Av          Mn   -5227.2822   0.9  -21711.2535  11256.6892  False
    Av          No  -33344.7931 0.001  -43914.9291 -22774.6571   True
    Av No Basement  -89425.4247 0.001 -113223.0046 -65627.8448   True
    Gd          Mn  -40773.5252 0.001  -59266.3889 -22280.6614   True
    Gd          No  -68891.0361 0.001  -82381.4947 -55400.5775   True
    Gd No Basement -124971.6677 0.001 -150202.3931 -99740.9423   True
    Mn          No  -28117.5109 0.001  -42367.1844 -13867.8374   True
    Mn No Basement  -84198.1425 0.001 -109842.8345 -58553.4505   True
    No No Basement  -56080.6316 0.001  -78388.8518 -33772.4114   True
---------------------------------------------------------------------


data.BsmtExposure.replace('Mn', 'Av', inplace = True)


data.BsmtExposure.value_counts()

No             1349
Av              482
Gd              181
No Basement      61
Name: BsmtExposure, dtype: int64


data.BsmtExposure.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ BsmtExposure', data = data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq          F        PR(>F)
BsmtExposure     3.0  1.247990e+12  4.159965e+11  106.77502  2.841040e-64
Residual      2069.0  8.060845e+12  3.896010e+09        NaN           NaN


t_BsmtExposure = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtExposure)
print(t_BsmtExposure)

         Multiple Comparison of Means - Tukey HSD, FWER=0.05          
======================================================================
group1    group2     meandiff   p-adj    lower        upper     reject
----------------------------------------------------------------------
    Av          Gd   37281.4404 0.001   23291.1417   51271.7391   True
    Av          No  -31609.5957 0.001  -40125.8321  -23093.3593   True
    Av No Basement  -87690.2273 0.001 -109499.6669  -65880.7876   True
    Gd          No  -68891.0361 0.001  -81594.8313  -56187.2409   True
    Gd No Basement -124971.6677 0.001 -148731.1245 -101212.2109   True
    No No Basement  -56080.6316 0.001   -77088.002  -35073.2612   True
----------------------------------------------------------------------


sns.countplot(data.BsmtExposure)
plt.title('Count plot of BsmtExposure')

Text(0.5, 1.0, 'Count plot of BsmtExposure')


sns.catplot('BsmtExposure', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtExposure vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of BsmtExposure vs Property_Sale_Price')


sns.boxplot('BsmtExposure', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtExposure vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of BsmtExposure vs Property_Sale_Price')


data.BsmtFinType1.isnull().sum()

59


data.BsmtFinType1.dtype

dtype('O')


data.BsmtFinType1.fillna('No basement', inplace = True)


data.BsmtFinType1.isnull().sum()

0


data.BsmtFinType1.value_counts()

Unf            604
GLQ            599
ALQ            319
BLQ            210
Rec            173
LwQ            109
No basement     59
Name: BsmtFinType1, dtype: int64


data.BsmtFinType1.value_counts().sum()

2073


sns.countplot(data.BsmtFinType1)
plt.title('Count plot of BsmtFinType1')

Text(0.5, 1.0, 'Count plot of BsmtFinType1')


data.groupby("BsmtFinType1")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BsmtFinType1', data = data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq           F         PR(>F)
BsmtFinType1     6.0  2.241398e+12  3.735663e+11  109.203384  8.185389e-120
Residual      2066.0  7.067436e+12  3.420831e+09         NaN            NaN


t_BsmtFinType1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType1)
print(t_BsmtFinType1)

           Multiple Comparison of Means - Tukey HSD, FWER=0.05            
==========================================================================
   group1      group2     meandiff  p-adj     lower        upper    reject
--------------------------------------------------------------------------
        ALQ         BLQ -13044.3965 0.1562   -28383.061    2294.268  False
        ALQ         GLQ  65336.0422  0.001   53372.0179  77300.0666   True
        ALQ         LwQ -10322.6489 0.6641  -29473.0553   8827.7575  False
        ALQ No basement -53278.6998  0.001  -77740.5472 -28816.8523   True
        ALQ         Rec -13260.3946 0.1983  -29558.1899   3037.4008  False
        ALQ         Unf  10049.4885 0.1662   -1897.3155  21996.2926  False
        BLQ         GLQ  78380.4388  0.001   64537.8951  92222.9825   True
        BLQ         LwQ   2721.7476    0.9   -17655.107  23098.6022  False
        BLQ No basement -40234.3032  0.001  -65667.7481 -14800.8584   True
        BLQ         Rec    -215.998    0.9  -17938.7594  17506.7633  False
        BLQ         Unf   23093.885  0.001     9266.222   36921.548   True
        GLQ         LwQ -75658.6912  0.001  -93633.1022 -57684.2801   True
        GLQ No basement -118614.742  0.001 -142167.3081 -95062.1759   True
        GLQ         Rec -78596.4368  0.001  -93494.7428 -63698.1309   True
        GLQ         Unf -55286.5537  0.001  -65239.8203 -45333.2872   True
        LwQ No basement -42956.0508  0.001  -70854.5016 -15057.6001   True
        LwQ         Rec  -2937.7457    0.9  -24046.0309  18170.5396  False
        LwQ         Unf  20372.1374 0.0146    2409.1838   38335.091   True
No basement         Rec  40018.3052  0.001   13995.1689  66041.4415   True
No basement         Unf  63328.1883  0.001   39784.3649  86872.0116   True
        Rec         Unf  23309.8831  0.001    8425.4023  38194.3639   True
--------------------------------------------------------------------------


data.BsmtFinType1.replace('LwQ', 'BLQ', inplace = True)
data.BsmtFinType1.replace('Rec', 'BLQ', inplace = True)
data.BsmtFinType1.replace('ALQ', 'Unf',inplace = True )


data.BsmtFinType1.value_counts()

Unf            923
GLQ            599
BLQ            492
No basement     59
Name: BsmtFinType1, dtype: int64


data.BsmtFinType1.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ BsmtFinType1', data = data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq           F         PR(>F)
BsmtFinType1     3.0  2.219637e+12  7.398789e+11  215.935521  7.410086e-122
Residual      2069.0  7.089197e+12  3.426388e+09         NaN            NaN


t_BsmtFinType1 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType1)
print(t_BsmtFinType1)

          Multiple Comparison of Means - Tukey HSD, FWER=0.05           
========================================================================
   group1      group2     meandiff  p-adj    lower       upper    reject
------------------------------------------------------------------------
        BLQ         GLQ  77853.4005 0.001  68696.3059  87010.4951   True
        BLQ No basement -40761.3415 0.001  -61496.556  -20026.127   True
        BLQ         Unf  19093.6216 0.001  10692.5225  27494.7207   True
        GLQ No basement -118614.742 0.001 -139150.693  -98078.791   True
        GLQ         Unf -58759.7789 0.001 -66656.2737 -50863.2841   True
No basement         Unf  59854.9631 0.001  39644.7833  80065.1429   True
------------------------------------------------------------------------


sns.countplot(data.BsmtFinType1)
plt.title('Count plot of BsmtFinType1')

Text(0.5, 1.0, 'Count plot of BsmtFinType1')


sns.catplot('BsmtFinType1', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtFinType1 vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of BsmtFinType1 vs Property_Sale_Price')


sns.boxplot('BsmtFinType1', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtFinType1 vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of BsmtFinType1 vs Property_Sale_Price')


data.BsmtFinSF1.isnull().sum()

0


data.BsmtFinSF1.describe()

count    2073.000000
mean      437.949349
std       448.051119
min         0.000000
25%         0.000000
50%       381.000000
75%       708.000000
max      5644.000000
Name: BsmtFinSF1, dtype: float64


data.BsmtFinSF1.value_counts()

0       663
24       18
16       15
936      12
1200      9
       ... 
1216      1
1198      1
1196      1
1186      1
1767      1
Name: BsmtFinSF1, Length: 637, dtype: int64


data.BsmtFinSF1.value_counts().sum()

2073


plt.hist(data.BsmtFinSF1)
plt.title('Hist of BsmtFinSF1')

Text(0.5, 1.0, 'Hist of BsmtFinSF1')


sns.distplot(data.BsmtFinSF1)
plt.title('Normality of BsmtFinSF1')

Text(0.5, 1.0, 'Normality of BsmtFinSF1')


sns.countplot(data.BsmtFinSF1)
plt.title('Count plot of BsmtFinSF1')

Text(0.5, 1.0, 'Count plot of BsmtFinSF1')


sns.boxplot(data.BsmtFinSF1, color = 'red')
plt.title('BoxPlot Of BsmtFinSF1')

Text(0.5, 1.0, 'BoxPlot Of BsmtFinSF1')


IQR = data.BsmtFinSF1.quantile(0.75) - data.BsmtFinSF1.quantile(0.25)
IQR

708.0


upper_limit = data.BsmtFinSF1.quantile(0.75) + (1.5*IQR)
upper_limit

1770.0


lower_limit = data.BsmtFinSF1.quantile(0.25) - (1.5*IQR)
lower_limit

-1062.0


len(data.BsmtFinSF1[data.BsmtFinSF1 > upper_limit])

12


len(data.BsmtFinSF1[data.BsmtFinSF1 < lower_limit])

0


for i in np.arange(upper_limit,2300,1000):
    outliers = len(data.BsmtFinSF1[data.BsmtFinSF1 > i])
    print('At the point of: ', i, 'There are ', outliers, 'Outliers')

At the point of:  1770.0 There are  12 Outliers


data.BsmtFinSF1 = np.where(data.BsmtFinSF1 > upper_limit, upper_limit, data.BsmtFinSF1)


sns.boxplot(data.BsmtFinSF1, color = 'g')
plt.title('BoxPlot Of BsmtFinSF1')

Text(0.5, 1.0, 'BoxPlot Of BsmtFinSF1')


np.corrcoef(data.BsmtFinSF1, data.Property_Sale_Price)

array([[1.        , 0.36562477],
       [0.36562477, 1.        ]])


data.BsmtFinType2.isnull().sum()

60


data.BsmtFinType2.dtype

dtype('O')


data.BsmtFinType2.fillna('No Basement', inplace = True)


data.BsmtFinType2.isnull().sum()

0


data.BsmtFinType2.value_counts()

Unf            1770
Rec              79
LwQ              68
No Basement      60
BLQ              50
ALQ              24
GLQ              22
Name: BsmtFinType2, dtype: int64


data.BsmtFinType2.value_counts().sum()

2073


sns.countplot(data.BsmtFinType2)
plt.title('Count plot of BsmtFinType2')

Text(0.5, 1.0, 'Count plot of BsmtFinType2')


data.groupby("BsmtFinType2")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BsmtFinType2', data = data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq          F        PR(>F)
BsmtFinType2     6.0  3.924698e+11  6.541164e+10  15.156452  4.704314e-17
Residual      2066.0  8.916364e+12  4.315762e+09        NaN           NaN


t_BsmtFinType2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType2)
print(t_BsmtFinType2)

           Multiple Comparison of Means - Tukey HSD, FWER=0.05            
==========================================================================
   group1      group2     meandiff  p-adj     lower        upper    reject
--------------------------------------------------------------------------
        ALQ         BLQ -53062.5067 0.0199 -101207.6947  -4917.3186   True
        ALQ         GLQ -17529.1667    0.9  -74754.6678  39696.3345  False
        ALQ         LwQ  -31972.549  0.385  -78004.7231   14059.625  False
        ALQ No Basement -89210.1167  0.001 -136036.0074 -42384.2259   True
        ALQ         Rec  -38026.635 0.1659  -83215.0487   7161.7787  False
        ALQ         Unf -17586.2023 0.8334  -57428.7054  22256.3009  False
        BLQ         GLQ    35533.34 0.3448  -14068.4736  85135.1536  False
        BLQ         LwQ  21089.9576 0.5863  -15028.4999  57208.4152  False
        BLQ No Basement   -36147.61 0.0624  -73272.3453    977.1253  False
        BLQ         Rec  15035.8716 0.8546  -20000.8899  50072.6332  False
        BLQ         Unf  35476.3044 0.0032    7673.3013  63279.3076   True
        GLQ         LwQ -14443.3824    0.9  -61996.9504  33110.1857  False
        GLQ No Basement   -71680.95  0.001 -120003.2515 -23358.6485   True
        GLQ         Rec -20497.4684 0.8383  -67234.7502  26239.8135  False
        GLQ         Unf    -57.0356    0.9  -41647.9702   41533.899  False
        LwQ No Basement -57237.5676  0.001  -91577.7466 -22897.3887   True
        LwQ         Rec   -6054.086    0.9  -38125.5153  26017.3433  False
        LwQ         Unf  14386.3468 0.5576   -9572.1361  38344.8297  False
No Basement         Rec  51183.4816  0.001   17982.8857  84384.0776   True
No Basement         Unf  71623.9144  0.001   46173.7298   97074.099   True
        Rec         Unf  20440.4328 0.0971   -1853.9743  42734.8399  False
--------------------------------------------------------------------------


data.BsmtFinType2.replace('GLQ', 'ALQ', inplace = True)
data.BsmtFinType2.replace('LwQ', 'Rec', inplace = True)
data.BsmtFinType2.replace('ALQ', 'Unf', inplace = True)
data.BsmtFinType2.replace('BLQ', 'Rec', inplace = True)


data.BsmtFinType2.value_counts()

Unf            1816
Rec             197
No Basement      60
Name: BsmtFinType2, dtype: int64


data.BsmtFinType2.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ BsmtFinType2', data = data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq          F        PR(>F)
BsmtFinType2     2.0  3.719368e+11  1.859684e+11  43.074752  4.695439e-19
Residual      2070.0  8.936897e+12  4.317342e+09        NaN           NaN


t_BsmtFinType2 = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFinType2)
print(t_BsmtFinType2)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
   group1   group2  meandiff  p-adj   lower      upper    reject
----------------------------------------------------------------
No Basement    Rec 49457.0058 0.001 26732.9501 72181.0616   True
No Basement    Unf 71857.0221 0.001 51635.6374 92078.4069   True
        Rec    Unf 22400.0163 0.001 10839.9886  33960.044   True
----------------------------------------------------------------


sns.countplot(data.BsmtFinType2)
plt.title('Count plot of BsmtFinType2')

Text(0.5, 1.0, 'Count plot of BsmtFinType2')


sns.catplot('BsmtFinType2', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtFinType2 vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of BsmtFinType2 vs Property_Sale_Price')


sns.boxplot('BsmtFinType2', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtFinType2 vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of BsmtFinType2 vs Property_Sale_Price')


data.BsmtFinSF2.isnull().sum()

0


data.BsmtFinSF2.describe()

count    2073.000000
mean       49.219489
std       165.621465
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max      1474.000000
Name: BsmtFinSF2, dtype: float64


data.BsmtFinSF2.value_counts()

0       1829
93         7
290        6
180        5
712        5
        ... 
600        1
580        1
546        1
544        1
1127       1
Name: BsmtFinSF2, Length: 144, dtype: int64


data.BsmtFinSF2.value_counts().sum()

2073


plt.hist(data.BsmtFinSF2)
plt.title('Hist of BsmtFinSF2')

Text(0.5, 1.0, 'Hist of BsmtFinSF2')


sns.distplot(data.BsmtFinSF1)
plt.title('Normality of BsmtFinSF1')

Text(0.5, 1.0, 'Normality of BsmtFinSF1')


sns.countplot(data.BsmtFinSF1)
plt.title('count plot of BsmtFinSF1')

Text(0.5, 1.0, 'count plot of BsmtFinSF1')


sns.boxplot(data.BsmtFinSF1, color = 'g')
plt.title('BoxPlot Of BsmtFinSF1')

Text(0.5, 1.0, 'BoxPlot Of BsmtFinSF1')


np.corrcoef(data.BsmtFinSF2, data.Property_Sale_Price)

array([[ 1.        , -0.02588584],
       [-0.02588584,  1.        ]])


data.BsmtUnfSF.isnull().sum()

0


data.BsmtUnfSF.describe()

count    2073.000000
mean      565.101302
std       449.730940
min         0.000000
25%       208.000000
50%       466.000000
75%       804.000000
max      2336.000000
Name: BsmtUnfSF, dtype: float64


data.BsmtUnfSF.value_counts()

0       173
300      11
728      11
600      11
384      10
       ... 
730       1
251       1
241       1
744       1
1969      1
Name: BsmtUnfSF, Length: 780, dtype: int64


data.BsmtUnfSF.value_counts().sum()

2073


plt.hist(data.BsmtUnfSF)
plt.title('Hist of BsmtUnfSF')

Text(0.5, 1.0, 'Hist of BsmtUnfSF')


sns.distplot(data.BsmtUnfSF)
plt.title('Normality of BsmtUnfSF')

Text(0.5, 1.0, 'Normality of BsmtUnfSF')


sns.countplot(data.BsmtUnfSF)
plt.title('count plot of BsmtUnfSF')

Text(0.5, 1.0, 'count plot of BsmtUnfSF')


sns.boxplot(data.BsmtUnfSF, color = 'red')
plt.title('BoxPlot Of BsmtUnfSF')

Text(0.5, 1.0, 'BoxPlot Of BsmtUnfSF')


IQR = data.BsmtUnfSF.quantile(0.75) - data.BsmtUnfSF.quantile(0.25)
IQR

596.0


upper_limit = data.BsmtUnfSF.quantile(0.75) + (1.5*IQR)
upper_limit

1698.0


lower_limit = data.BsmtUnfSF.quantile(0.25) - (1.5*IQR)
lower_limit

-686.0


len(data.BsmtUnfSF[data.BsmtUnfSF > upper_limit])

41


len(data.BsmtUnfSF[data.BsmtUnfSF < lower_limit])

0


for i in np.arange(upper_limit,2700,300):
    outliers = len(data.BsmtUnfSF[data.BsmtUnfSF > i])
    print('At the point of: ', i, 'There are ', outliers, 'Outliers')

At the point of:  1698.0 There are  41 Outliers
At the point of:  1998.0 There are  6 Outliers
At the point of:  2298.0 There are  1 Outliers
At the point of:  2598.0 There are  0 Outliers


data.BsmtUnfSF = np.where(data.BsmtUnfSF > upper_limit, upper_limit, data.BsmtUnfSF)


sns.boxplot(data.BsmtUnfSF, color = 'g')
plt.title('BoxPlot Of BsmtUnfSF')

Text(0.5, 1.0, 'BoxPlot Of BsmtUnfSF')


np.corrcoef(data.BsmtUnfSF, data.Property_Sale_Price)

array([[1.        , 0.23819431],
       [0.23819431, 1.        ]])


data.TotalBsmtSF.isnull().sum()

0


data.TotalBsmtSF.describe()

count    2073.000000
mean     1052.270140
std       431.026436
min         0.000000
25%       794.000000
50%       990.000000
75%      1291.000000
max      6110.000000
Name: TotalBsmtSF, dtype: float64


data.TotalBsmtSF.value_counts()

0       59
864     48
816     21
912     21
672     21
        ..
2046     1
2042     1
6110     1
2006     1
2035     1
Name: TotalBsmtSF, Length: 721, dtype: int64


data.TotalBsmtSF.value_counts().sum()

2073


plt.hist(data.TotalBsmtSF)
plt.title('Hist of TotalBsmtSF')

Text(0.5, 1.0, 'Hist of TotalBsmtSF')


sns.distplot(data.TotalBsmtSF)
plt.title('Normality of TotalBsmtSF')

Text(0.5, 1.0, 'Normality of TotalBsmtSF')


sns.countplot(data.TotalBsmtSF)
plt.title('count plot of TotalBsmtSF')

Text(0.5, 1.0, 'count plot of TotalBsmtSF')


sns.boxplot(data.TotalBsmtSF, color = 'red')
plt.title('BoxPlot Of TotalBsmtSF')

Text(0.5, 1.0, 'BoxPlot Of TotalBsmtSF')


IQR = data.TotalBsmtSF.quantile(0.75) - data.TotalBsmtSF.quantile(0.25)
IQR

497.0


upper_limit = data.TotalBsmtSF.quantile(0.75) + (1.5*IQR)
upper_limit

2036.5


lower_limit = data.TotalBsmtSF.quantile(0.25) - (1.5*IQR)
lower_limit

48.5


len(data.TotalBsmtSF[data.TotalBsmtSF > upper_limit])

30


len(data.TotalBsmtSF[data.TotalBsmtSF < lower_limit])

59


for i in np.arange(upper_limit, 3000, 350):
    outliers = len(data.TotalBsmtSF[data.TotalBsmtSF > i])
    print('At the point of: ', i, 'There are', outliers, 'Outliers')

At the point of:  2036.5 There are 30 Outliers
At the point of:  2386.5 There are 11 Outliers
At the point of:  2736.5 There are 5 Outliers


data.TotalBsmtSF = np.where(data.TotalBsmtSF > upper_limit, upper_limit, data.TotalBsmtSF)


data.TotalBsmtSF = np.where(data.TotalBsmtSF < lower_limit, lower_limit, data.TotalBsmtSF)


sns.boxplot(data.TotalBsmtSF, color = 'g')
plt.title('BoxPlot Of TotalBsmtSF')

Text(0.5, 1.0, 'BoxPlot Of TotalBsmtSF')


np.corrcoef(data.TotalBsmtSF, data.Property_Sale_Price)

array([[1.        , 0.64253607],
       [0.64253607, 1.        ]])


sns.scatterplot(x = data['TotalBsmtSF'], y = data.Property_Sale_Price, data = data )
plt.xlabel('TotalBsmtSF')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter plot of TotalBsmtSF vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter plot of TotalBsmtSF vs Property_Sale_Price')


data.Heating.isnull().sum()

0


data.Heating.value_counts()

GasA     2025
GasW       25
Grav       10
Wall        8
Floor       3
OthW        2
Name: Heating, dtype: int64


data.Heating.value_counts().sum()

2073


sns.countplot(data.Heating)
plt.title('Count plot of Heating')

Text(0.5, 1.0, 'Count plot of Heating')


data.groupby("Heating")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Heating', data=data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq          F        PR(>F)
Heating      5.0  2.214873e+11  4.429745e+10  10.075859  1.514722e-09
Residual  2067.0  9.087347e+12  4.396394e+09        NaN           NaN


t_Heating = pairwise_tukeyhsd(data.Property_Sale_Price, data.Heating, alpha = 0.05)
print(t_Heating)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
 Floor   GasA  106406.6864 0.0616   -2866.2347 215679.6076  False
 Floor   GasW     84870.32 0.2903  -30687.7029 200428.3429  False
 Floor   Grav       -130.0    0.9 -124628.1126 124368.1126  False
 Floor   OthW      53250.0    0.9 -119397.8188 225897.8188  False
 Floor   Wall      19550.0    0.9 -108489.0492 147589.0492  False
  GasA   GasW  -21536.3664 0.5768  -59594.3813  16521.6484  False
  GasA   Grav -106536.6864  0.001 -166491.1348  -46582.238   True
  GasA   OthW  -53156.6864 0.8544 -186955.1362  80641.7634  False
  GasA   Wall  -86856.6864 0.0031 -153854.8503 -19858.5225   True
  GasW   Grav    -85000.32 0.0082 -155764.8679 -14235.7721   True
  GasW   OthW    -31620.32    0.9 -170599.1332 107358.4932  False
  GasW   Wall    -65320.32 0.1479 -142143.7495  11503.1095  False
  Grav   OthW      53380.0    0.9  -93116.5321 199876.5321  False
  Grav   Wall      19680.0    0.9  -70030.4382 109390.4382  False
  OthW   Wall     -33700.0    0.9  -183217.397  115817.397  False
-----------------------------------------------------------------


data["Heating"].replace('Floor', 'Grav',inplace = True)
data["Heating"].replace('OthW', 'Grav',inplace = True)
data["Heating"].replace('Wall', 'Grav',inplace = True)
data["Heating"].replace('GasW', 'GasA',inplace = True)


data.Heating.value_counts()

GasA    2050
Grav      23
Name: Heating, dtype: int64


data.Heating.value_counts().sum()

2073


df1=data[data.Heating=='GasA']

df2=data[data.Heating=='Grav']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=6.816687704976715, pvalue=1.2177323085835995e-11)


sns.countplot(data.Heating)
plt.title('count plot of Heating')

Text(0.5, 1.0, 'count plot of Heating')


sns.catplot('Heating', 'Property_Sale_Price', data=data)
plt.title('Cat plot of Heating vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of Heating vs Property_Sale_Price')


sns.boxplot('Heating', 'Property_Sale_Price', data=data)
plt.title('Box plot of Heating vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of Heating vs Property_Sale_Price')


data.HeatingQC.isnull().sum()

0


data.HeatingQC.value_counts()

Ex    1045
TA     618
Gd     341
Fa      68
Po       1
Name: HeatingQC, dtype: int64


data.HeatingQC.value_counts().sum()

2073


sns.countplot(data.HeatingQC)
plt.title('Count plot of HeatingQC')

Text(0.5, 1.0, 'Count plot of HeatingQC')


data.groupby("HeatingQC")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ HeatingQC', data=data).fit()
print(sm.stats.anova_lm(mod))

               df        sum_sq       mean_sq          F         PR(>F)
HeatingQC     4.0  2.147424e+12  5.368561e+11  155.02791  4.074768e-116
Residual   2068.0  7.161410e+12  3.462964e+09        NaN            NaN


t_HeatingQC = pairwise_tukeyhsd(data.Property_Sale_Price, data.HeatingQC, alpha = 0.05)
print(t_HeatingQC)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
    Ex     Fa  -87996.5019  0.001 -108103.5781 -67889.4258   True
    Ex     Gd  -49799.7849  0.001  -59819.6119 -39779.9579   True
    Ex     Po -121654.0019 0.2353 -282393.1414  39085.1376  False
    Ex     TA  -66833.6653  0.001  -74986.4822 -58680.8485   True
    Fa     Gd    38196.717  0.001   16859.1966  59534.2375   True
    Fa     Po     -33657.5    0.9 -195496.8148 128181.8148  False
    Fa     TA   21162.8366 0.0395     635.7504  41689.9228   True
    Gd     Po   -71854.217 0.7134 -232751.9057  89043.4717  False
    Gd     TA  -17033.8804  0.001  -27871.9471  -6195.8138   True
    Po     TA   54820.3366  0.877 -105971.8825 215612.5557  False
-----------------------------------------------------------------


data["HeatingQC"].replace('Po', 'Fa',inplace = True)


data.HeatingQC.value_counts()

Ex    1045
TA     618
Gd     341
Fa      69
Name: HeatingQC, dtype: int64


data.HeatingQC.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ HeatingQC', data=data).fit()
print(sm.stats.anova_lm(mod))

               df        sum_sq       mean_sq           F         PR(>F)
HeatingQC     3.0  2.146308e+12  7.154360e+11  206.664102  3.061274e-117
Residual   2069.0  7.162526e+12  3.461830e+09         NaN            NaN


t_HeatingQC = pairwise_tukeyhsd(data.Property_Sale_Price, data.HeatingQC, alpha = 0.05)
print(t_HeatingQC)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
    Ex     Fa -88484.2918  0.001 -107287.6653 -69680.9182   True
    Ex     Gd -49799.7849  0.001  -59234.3557 -40365.2141   True
    Ex     TA -66833.6653  0.001  -74510.2777  -59157.053   True
    Fa     Gd  38684.5069  0.001   18715.0588  58653.9549   True
    Fa     TA  21650.6264 0.0198    2449.1133  40852.1396   True
    Gd     TA -17033.8804  0.001  -27238.8977  -6828.8631   True
----------------------------------------------------------------


sns.countplot(data.HeatingQC)
plt.title('Count plot of HeatingQC')

Text(0.5, 1.0, 'Count plot of HeatingQC')


sns.catplot('HeatingQC', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of HeatingQC vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of HeatingQC vs Property_Sale_Price')


sns.boxplot('HeatingQC', 'Property_Sale_Price', data=data)
plt.title('Box Plot of HeatingQC vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of HeatingQC vs Property_Sale_Price')


data.CentralAir.isnull().sum()

0


data.CentralAir.value_counts()

Y    1935
N     138
Name: CentralAir, dtype: int64


data.CentralAir.value_counts().sum()

2073


sns.countplot(data.CentralAir)
plt.title('Count plot of CentralAir')

Text(0.5, 1.0, 'Count plot of CentralAir')


data.groupby("CentralAir")["Property_Sale_Price"].describe()


df1=data[data.CentralAir=='Y']
df2=data[data.CentralAir=='N']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=13.530853321902182, pvalue=4.904316035430789e-40)


sns.catplot('CentralAir', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of CentralAir vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of CentralAir vs Property_Sale_Price')


sns.boxplot('CentralAir', 'Property_Sale_Price', data=data)
plt.title('Box Plot of CentralAir vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of CentralAir vs Property_Sale_Price')


data.Electrical.isnull().sum()

1


data.Electrical.dtype

dtype('O')


data.Electrical.mode()

0    SBrkr
dtype: object


data.Electrical.fillna(data.Electrical.mode()[0], inplace = True)


data.Electrical.isnull().sum()

0


data.Electrical.value_counts()

SBrkr    1902
FuseA     127
FuseF      40
FuseP       3
Mix         1
Name: Electrical, dtype: int64


data.Electrical.value_counts().sum()

2073


sns.countplot(data.Electrical)
plt.title('Count plot of Electrical')

Text(0.5, 1.0, 'Count plot of Electrical')


data.groupby("Electrical")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Electrical', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq         F        PR(>F)
Electrical     4.0  6.580760e+11  1.645190e+11  39.32896  8.832416e-32
Residual    2068.0  8.650758e+12  4.183152e+09       NaN           NaN


t_B = pairwise_tukeyhsd(data.Property_Sale_Price, data.Electrical, alpha = 0.05)
print(t_B)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
 FuseA  FuseF -14329.7344 0.7119  -46345.8134  17686.3445  False
 FuseA  FuseP -25431.0761    0.9 -128576.7343  77714.5821  False
 FuseA    Mix -55764.4094    0.9 -233038.3455 121509.5266  False
 FuseA  SBrkr  60128.2988  0.001   43944.6788  76311.9187   True
 FuseF  FuseP -11101.3417    0.9 -116803.8682  94601.1849  False
 FuseF    Mix  -41434.675    0.9 -220208.4027 137339.0527  False
 FuseF  SBrkr  74458.0332  0.001   46246.2119 102669.8545   True
 FuseP    Mix -30333.3333    0.9 -234230.4721 173563.8055  False
 FuseP  SBrkr  85559.3749 0.1486  -16469.5639 187588.3137  False
   Mix  SBrkr 115892.7082 0.3799  -60733.8072 292519.2237  False
----------------------------------------------------------------


data["Electrical"].replace('FuseP', 'FuseA',inplace = True)
data["Electrical"].replace('Mix', 'FuseA',inplace = True)
data["Electrical"].replace('FuseF', 'FuseA',inplace = True)


data.Electrical.value_counts()

SBrkr    1902
FuseA     171
Name: Electrical, dtype: int64


data.Electrical.value_counts().sum()

2073


df1=data[data.Electrical=='SBrkr']

df2=data[data.Electrical=='FuseA']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=12.445066649361097, pvalue=2.4338317708885697e-34)


sns.countplot(data.Electrical)
plt.title('Count plot of Electrical')

Text(0.5, 1.0, 'Count plot of Electrical')


sns.catplot('Electrical', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of Electrical vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Electrical vs Property_Sale_Price')


sns.boxplot('Electrical', 'Property_Sale_Price', data=data)
plt.title('Box Plot of Electrical vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Electrical vs Property_Sale_Price')


data.FirstFlrSF.isnull().sum()

0


data.FirstFlrSF.value_counts()

864     33
1040    23
912     19
894     17
848     16
        ..
1898     1
1888     1
1872     1
1856     1
1987     1
Name: FirstFlrSF, Length: 753, dtype: int64


data.FirstFlrSF.value_counts().sum()

2073


plt.hist(data.FirstFlrSF)
plt.title('Hist of FirstFlrSF')

Text(0.5, 1.0, 'Hist of FirstFlrSF')


sns.distplot(data.FirstFlrSF)
plt.title('Normality of FirstFlrSF')

Text(0.5, 1.0, 'Normality of FirstFlrSF')


sns.countplot(data.FirstFlrSF)
plt.title('count plot of FirstFlrSF')

Text(0.5, 1.0, 'count plot of FirstFlrSF')


sns.boxplot(data['FirstFlrSF'], color = 'red')
plt.title('BoxPlot Of FirstFlrSF')

Text(0.5, 1.0, 'BoxPlot Of FirstFlrSF')


IQR = data['FirstFlrSF'].quantile(0.75) - data['FirstFlrSF'].quantile(0.25)
IQR

497.0


upper_limit = data['FirstFlrSF'].quantile(0.75) + (1.5*IQR)
upper_limit

2136.5


len(data['FirstFlrSF'][data['FirstFlrSF'] > upper_limit])

26


for i in np.arange(upper_limit, 3000, 500):
    outliers = len(data['FirstFlrSF'][data['FirstFlrSF'] > i])
    print('At the point of: ', i, 'There are ', outliers, 'outliers' )

At the point of:  2136.5 There are  26 outliers
At the point of:  2636.5 There are  5 outliers


data['FirstFlrSF'] = np.where(data['FirstFlrSF'] > upper_limit, upper_limit, data['FirstFlrSF'])


sns.boxplot(data['FirstFlrSF'], color = 'g')
plt.title('BoxPlot Of FirstFlrSF')

Text(0.5, 1.0, 'BoxPlot Of FirstFlrSF')


sns.distplot(data['FirstFlrSF'])
plt.title('Normality of FirstFlrSF')

Text(0.5, 1.0, 'Normality of FirstFlrSF')


np.corrcoef(data['FirstFlrSF'], data.Property_Sale_Price)

array([[1.        , 0.60991825],
       [0.60991825, 1.        ]])


sns.scatterplot('FirstFlrSF', 'Property_Sale_Price', data=data)

<AxesSubplot:xlabel='FirstFlrSF', ylabel='Property_Sale_Price'>


data['SecondFlrSF'].isnull().sum()

0


data['SecondFlrSF'].describe()

count    2073.000000
mean      344.745779
std       438.844748
min         0.000000
25%         0.000000
50%         0.000000
75%       730.000000
max      2065.000000
Name: SecondFlrSF, dtype: float64


data['SecondFlrSF'].value_counts()

0       1195
546       13
728       11
896       10
504       10
        ... 
1230       1
1242       1
1274       1
1286       1
1611       1
Name: SecondFlrSF, Length: 417, dtype: int64


data['SecondFlrSF'].value_counts().sum()

2073


plt.hist(data['SecondFlrSF'])
plt.title('Hist of SecondFlrSF')

Text(0.5, 1.0, 'Hist of SecondFlrSF')


sns.distplot(data['SecondFlrSF'])
plt.title('Mormality of SecondFlrSF')

Text(0.5, 1.0, 'Mormality of SecondFlrSF')


sns.countplot(data['SecondFlrSF'])
plt.title('Count plot of SecondFlrSF')

Text(0.5, 1.0, 'Count plot of SecondFlrSF')


sns.boxplot(data['SecondFlrSF'], color = 'red')
plt.title('BoxPlot Of SecondFlrSF')

Text(0.5, 1.0, 'BoxPlot Of SecondFlrSF')


IQR = data['SecondFlrSF'].quantile(0.75) - data['SecondFlrSF'].quantile(0.25)
IQR

730.0


upper_limit = data['SecondFlrSF'].quantile(0.75) + (1.5*IQR)
upper_limit

1825.0


lower_limit = data['SecondFlrSF'].quantile(0.25) - (1.5*IQR)
lower_limit

-1095.0


len(data['SecondFlrSF'][data['SecondFlrSF'] > upper_limit])

2


len(data['SecondFlrSF'][data['SecondFlrSF'] < lower_limit])

0


for i in np.arange(upper_limit, 2500, 518):
    outliers = len(data['SecondFlrSF'][data['SecondFlrSF'] > i])
    print('At the point of: ', i, 'There are', outliers, 'outliers')

At the point of:  1825.0 There are 2 outliers
At the point of:  2343.0 There are 0 outliers


data['SecondFlrSF'] = np.where(data['SecondFlrSF'] > upper_limit, upper_limit, data['SecondFlrSF'])


sns.boxplot(data['SecondFlrSF'], color = 'g')
plt.title('BoxPlot Of SecondFlrSF')

Text(0.5, 1.0, 'BoxPlot Of SecondFlrSF')


sns.distplot(data['SecondFlrSF'])
plt.title('Mormality of SecondFlrSF')

Text(0.5, 1.0, 'Mormality of SecondFlrSF')


np.corrcoef(data['SecondFlrSF'], data.Property_Sale_Price)

array([[1.        , 0.33383669],
       [0.33383669, 1.        ]])


sns.scatterplot('SecondFlrSF', 'Property_Sale_Price', data=data)

<AxesSubplot:xlabel='SecondFlrSF', ylabel='Property_Sale_Price'>


data.LowQualFinSF.isnull().sum()

0


data.LowQualFinSF.describe()

count    2073.000000
mean        6.168355
std        49.553967
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max       572.000000
Name: LowQualFinSF, dtype: float64


data.LowQualFinSF.value_counts()

0      2034
360       6
80        3
120       3
513       2
481       2
397       2
53        2
514       2
384       2
232       2
390       1
392       1
420       1
528       1
572       1
234       1
205       1
371       1
473       1
479       1
156       1
144       1
515       1
Name: LowQualFinSF, dtype: int64


data.LowQualFinSF.value_counts().sum()

2073


plt.hist(data.LowQualFinSF)
plt.title('Hist of LowQualFinSF')

Text(0.5, 1.0, 'Hist of LowQualFinSF')


sns.distplot(data.LowQualFinSF)
plt.title('Normality of LowQualFinSF')

Text(0.5, 1.0, 'Normality of LowQualFinSF')


sns.countplot(data.LowQualFinSF)
plt.title('Count plot of LowQualFinSF')

Text(0.5, 1.0, 'Count plot of LowQualFinSF')


sns.boxplot(data.LowQualFinSF, color = 'red')
plt.title('BoxPlot Of LowQualFinSF')

Text(0.5, 1.0, 'BoxPlot Of LowQualFinSF')


IQR = data['LowQualFinSF'].quantile(0.75) - data['LowQualFinSF'].quantile(0.25)
IQR

0.0


upper_limit = data['LowQualFinSF'].quantile(0.75) + (1.5*IQR)
upper_limit

0.0


lower_limit = data['LowQualFinSF'].quantile(0.25) - (1.5*IQR)
lower_limit

0.0


data.GrLivArea.isnull().sum()

0


data.GrLivArea.describe()

count    2073.000000
mean     1517.011095
std       517.236428
min       334.000000
25%      1126.000000
50%      1470.000000
75%      1792.000000
max      5642.000000
Name: GrLivArea, dtype: float64


data.GrLivArea.value_counts()

864     28
1040    20
894     16
848     13
1200    12
        ..
2704     1
790      1
2668     1
2654     1
2035     1
Name: GrLivArea, Length: 861, dtype: int64


data.GrLivArea.value_counts().sum()

2073


plt.hist(data.GrLivArea)
plt.title('Hit of GrLivArea')

Text(0.5, 1.0, 'Hit of GrLivArea')


sns.distplot(data.GrLivArea)
plt.title('Normality of GrLivArea')

Text(0.5, 1.0, 'Normality of GrLivArea')


sns.countplot(data.GrLivArea)
plt.title('Count plot of GrLivArea')

Text(0.5, 1.0, 'Count plot of GrLivArea')


sns.boxplot(data.GrLivArea, color = 'red')
plt.title('BoxPlot Of GrLivArea')

Text(0.5, 1.0, 'BoxPlot Of GrLivArea')


IQR = data.GrLivArea.quantile(0.75)- data.GrLivArea.quantile(0.25)
IQR

666.0


upper_limit = data.GrLivArea.quantile(0.75) + (1.5*IQR)
upper_limit

2791.0


lower_limit = data.GrLivArea.quantile(0.25) -(1.5*IQR)
lower_limit

127.0


len(data.GrLivArea[data.GrLivArea > upper_limit])

39


len(data.GrLivArea[data.GrLivArea < lower_limit])

0


for i in np.arange(upper_limit, 4800, 500):
    outliers = len(data.GrLivArea[data.GrLivArea > i])
    print('At the point of: ', i, 'There are', outliers, 'outliers')

At the point of:  2791.0 There are 39 outliers
At the point of:  3291.0 There are 9 outliers
At the point of:  3791.0 There are 4 outliers
At the point of:  4291.0 There are 4 outliers
At the point of:  4791.0 There are 1 outliers


data.GrLivArea = np.where(data.GrLivArea > upper_limit, upper_limit, data.GrLivArea)


sns.boxplot(data.GrLivArea, color = 'g')
plt.title('BoxPlot Of GrLivArea')

Text(0.5, 1.0, 'BoxPlot Of GrLivArea')


sns.distplot(data.GrLivArea)
plt.title('Normality of GrLivArea')

Text(0.5, 1.0, 'Normality of GrLivArea')


np.corrcoef(data.GrLivArea, data.Property_Sale_Price)

array([[1.        , 0.73086878],
       [0.73086878, 1.        ]])


sns.scatterplot(data.GrLivArea, data.Property_Sale_Price)
plt.title('GrLivArea vs Property_Sale_Price')

Text(0.5, 1.0, 'GrLivArea vs Property_Sale_Price')


data.BsmtFullBath.isnull().sum()

0


data.BsmtFullBath.describe()

count    2073.000000
mean        0.429329
std         0.522605
min         0.000000
25%         0.000000
50%         0.000000
75%         1.000000
max         3.000000
Name: BsmtFullBath, dtype: float64


data.BsmtFullBath.value_counts()

0    1210
1     838
2      23
3       2
Name: BsmtFullBath, dtype: int64


data.BsmtFullBath.value_counts().sum()

2073


sns.countplot(data.BsmtFullBath)
plt.title('Count plot of BsmtFullBath')

Text(0.5, 1.0, 'Count plot of BsmtFullBath')


data.groupby("BsmtFullBath")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BsmtFullBath', data=data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq           F        PR(>F)
BsmtFullBath     1.0  4.969889e+11  4.969889e+11  116.804584  1.592558e-26
Residual      2071.0  8.811845e+12  4.254875e+09         NaN           NaN


t_BsmtFullBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.BsmtFullBath)
print(t_BsmtFullBath)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
     0      1  31072.8891  0.001   23537.9572  38607.8209   True
     0      2  50817.9173 0.0013   15527.7285  86108.1061   True
     0      3  14546.3521    0.9 -104104.9952 133197.6993  False
     1      2  19745.0282 0.4798   -15690.972  55181.0284  False
     1      3  -16526.537    0.9 -135221.3342 102168.2602  False
     2      3 -36271.5652 0.8617  -159872.039  87328.9085  False
----------------------------------------------------------------


data["BsmtFullBath"].replace(0, 3,inplace = True)
data["BsmtFullBath"].replace(2, 1,inplace = True)


data.BsmtFullBath.value_counts()

3    1212
1     861
Name: BsmtFullBath, dtype: int64


data.BsmtFullBath.value_counts().sum()

2073


df1=data[data.BsmtFullBath==3]

df2=data[data.BsmtFullBath==1]

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=-10.864081506501448, pvalue=8.882352183807123e-27)


sns.countplot(data.BsmtFullBath)
plt.title('Count plot of BsmtFullBath')

Text(0.5, 1.0, 'Count plot of BsmtFullBath')


sns.catplot('BsmtFullBath', 'Property_Sale_Price', data=data)
plt.title('Cat plot of BsmtFullBath vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat plot of BsmtFullBath vs Property_Sale_Price')


sns.boxplot('BsmtFullBath', 'Property_Sale_Price', data=data)
plt.title('Box plot of BsmtFullBath vs Property_Sale_Price')

Text(0.5, 1.0, 'Box plot of BsmtFullBath vs Property_Sale_Price')


data.BsmtHalfBath.isnull().sum()

0


data.BsmtHalfBath.describe()

count    2073.000000
mean        0.056440
std         0.237015
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max         2.000000
Name: BsmtHalfBath, dtype: float64


data.BsmtHalfBath.value_counts()

0    1959
1     111
2       3
Name: BsmtHalfBath, dtype: int64


data.BsmtHalfBath.value_counts().sum()

2073


sns.countplot(data.BsmtHalfBath)
plt.title('Count plot of BsmtHalfBath')

Text(0.5, 1.0, 'Count plot of BsmtHalfBath')


data.groupby("BsmtHalfBath")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BsmtHalfBath', data=data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq         F    PR(>F)
BsmtHalfBath     1.0  9.974184e+09  9.974184e+09  2.221405  0.136261
Residual      2071.0  9.298860e+12  4.490034e+09       NaN       NaN


data.FullBath.isnull().sum()

0


data.FullBath.describe()

count    2073.000000
mean        1.571635
std         0.545963
min         0.000000
25%         1.000000
50%         2.000000
75%         2.000000
max         3.000000
Name: FullBath, dtype: float64


data.FullBath.value_counts()

2    1117
1     901
3      41
0      14
Name: FullBath, dtype: int64


data.FullBath.value_counts().sum()

2073


sns.countplot(data.FullBath)
plt.title('Count plot of FullBath')

Text(0.5, 1.0, 'Count plot of FullBath')


data.groupby("FullBath")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ FullBath', data=data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq            F         PR(>F)
FullBath     1.0  3.046506e+12  3.046506e+12  1007.503049  1.639697e-180
Residual  2071.0  6.262328e+12  3.023818e+09          NaN            NaN


t_FullBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.FullBath)
print(t_FullBath)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
     0      1 -46600.5686 0.0077 -84104.9235  -9096.2136   True
     0      2  27609.6111 0.2303  -9839.2198   65058.442  False
     0      3 105269.4355  0.001  62164.9005 148373.9706   True
     1      2  74210.1797  0.001  67974.7101  80445.6492   True
     1      3 151870.0041  0.001 129633.3882   174106.62   True
     2      3  77659.8245  0.001   55516.984  99802.6649   True
---------------------------------------------------------------


data["FullBath"].replace(0, 2,inplace = True)


data.FullBath.value_counts()

2    1131
1     901
3      41
Name: FullBath, dtype: int64


data.FullBath.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ FullBath', data=data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq            F         PR(>F)
FullBath     1.0  3.228811e+12  3.228811e+12  1099.809517  8.243835e-194
Residual  2071.0  6.080023e+12  2.935791e+09          NaN            NaN


t_FullBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.FullBath)
print(t_FullBath)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1 group2   meandiff  p-adj    lower       upper    reject
--------------------------------------------------------------
     1      2  73868.4161 0.001  68192.5205  79544.3117   True
     1      3 151870.0041 0.001 131572.7461 172167.2622   True
     2      3   78001.588 0.001  57794.3563  98208.8197   True
--------------------------------------------------------------


sns.countplot(data.FullBath)
plt.title('Count plot of FullBath')

Text(0.5, 1.0, 'Count plot of FullBath')


sns.catplot('FullBath', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of FullBath vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of FullBath vs Property_Sale_Price')


sns.boxplot('FullBath', 'Property_Sale_Price', data=data)
plt.title('Box Plot of FullBath vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of FullBath vs Property_Sale_Price')


data.HalfBath.isnull().sum()

0


data.HalfBath.describe()

count    2073.000000
mean        0.378678
std         0.499873
min         0.000000
25%         0.000000
50%         0.000000
75%         1.000000
max         2.000000
Name: HalfBath, dtype: float64


data.HalfBath.value_counts()

0    1303
1     755
2      15
Name: HalfBath, dtype: int64


data.HalfBath.value_counts().sum()

2073


sns.countplot(data.HalfBath)
plt.title('Count plot of HalfBath')

Text(0.5, 1.0, 'Count plot of HalfBath')


data.groupby("HalfBath")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ HalfBath', data=data).fit()
print(sm.stats.anova_lm(mod))

              df        sum_sq       mean_sq           F        PR(>F)
HalfBath     1.0  8.827791e+11  8.827791e+11  216.974087  8.835252e-47
Residual  2071.0  8.426055e+12  4.068592e+09         NaN           NaN


t_HalfBath = pairwise_tukeyhsd(data.Property_Sale_Price, data.HalfBath)
print(t_HalfBath)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1 group2   meandiff  p-adj     lower      upper    reject
--------------------------------------------------------------
     0      1  45720.1319  0.001  38925.7075 52514.5563   True
     0      2  -2818.2619    0.9 -41394.1147 35757.5909  False
     1      2 -48538.3938 0.0093  -87273.248 -9803.5397   True
--------------------------------------------------------------


data["HalfBath"].replace(2, 0,inplace = True)


data.HalfBath.value_counts()

0    1318
1     755
Name: HalfBath, dtype: int64


data.HalfBath.value_counts().sum()

2073


df1=data[data.HalfBath==0]

df2=data[data.HalfBath==1]

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=-15.83031334361822, pvalue=2.276986553597032e-53)


sns.countplot(data.HalfBath)
plt.title('Count plot of HalfBath')

Text(0.5, 1.0, 'Count plot of HalfBath')


sns.catplot('HalfBath', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of HalfBath vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of HalfBath vs Property_Sale_Price')


sns.boxplot('HalfBath', 'Property_Sale_Price', data=data)
plt.title('Box Plot of HalfBath vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of HalfBath vs Property_Sale_Price')


data.BedroomAbvGr.isnull().sum()

0


data.BedroomAbvGr.describe()

count    2073.000000
mean        2.874096
std         0.816144
min         0.000000
25%         2.000000
50%         3.000000
75%         3.000000
max         8.000000
Name: BedroomAbvGr, dtype: float64


data.BedroomAbvGr.value_counts()

3    1131
2     503
4     319
1      71
5      30
0       9
6       9
8       1
Name: BedroomAbvGr, dtype: int64


data.BedroomAbvGr.value_counts().sum()

2073


sns.countplot(data.BedroomAbvGr)
plt.title('Count plot of BedroomAbvGr')

Text(0.5, 1.0, 'Count plot of BedroomAbvGr')


data.groupby("BedroomAbvGr")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ BedroomAbvGr', data=data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq          F        PR(>F)
BedroomAbvGr     1.0  2.869680e+11  2.869680e+11  65.874481  8.152662e-16
Residual      2071.0  9.021866e+12  4.356285e+09        NaN           NaN


t_BedroomAbvGr = pairwise_tukeyhsd(data.Property_Sale_Price, data.BedroomAbvGr)
print(t_BedroomAbvGr)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
     0      1  -93469.5571 0.0013 -163181.0252  -23758.089   True
     0      2 -101071.2039  0.001 -167329.3679 -34813.0399   True
     0      3  -75813.4887 0.0116 -141747.5052  -9879.4721   True
     0      4   -50314.388 0.2983 -116907.6042  16278.8281  False
     0      5  -73035.4444 0.0621 -147914.4531   1843.5642  False
     0      6    -112400.0  0.006 -205275.9796 -19524.0204   True
     0      8  -55995.4444    0.9 -263672.4483 151681.5594  False
     1      2   -7601.6468    0.9  -32579.3467  17376.0531  False
     1      3   17656.0685 0.3388   -6448.6144  41760.7513  False
     1      4   43155.1691  0.001   17301.7818  69008.5564   True
     1      5   20434.1127 0.8153  -22468.1848  63336.4101  False
     1      6  -18930.4429    0.9   -88641.911  50781.0252  False
     1      8   37474.1127    0.9 -160928.2037  235876.429  False
     2      3   25257.7152  0.001   14698.7735   35816.657   True
     2      4   50756.8159  0.001   36655.2858   64858.346   True
     2      5   28035.7594 0.2956   -8992.1036  65063.6225  False
     2      6  -11328.7961    0.9  -77586.9601  54929.3679  False
     2      8   45075.7594    0.9 -152139.6929 242291.2118  False
     3      4   25499.1006  0.001   13008.9688  37989.2324   True
     3      5    2778.0442    0.9  -33666.6109  39222.6993  False
     3      6  -36586.5113 0.6729 -102520.5279  29347.5052  False
     3      8   19818.0442    0.9 -177288.7413 216924.8297  False
     4      5  -22721.0564 0.5841  -60345.1815  14903.0686  False
     4      6   -62085.612 0.0883 -128678.8281   4507.6042  False
     4      8   -5681.0564    0.9 -203009.3281 191647.2152  False
     5      6  -39364.5556 0.7258 -114243.5642  35514.4531  False
     5      8      17040.0    0.9 -183236.4496 217316.4496  False
     6      8   56404.5556    0.9 -151272.4483 264081.5594  False
-----------------------------------------------------------------


data["BedroomAbvGr"].replace(8, 0,inplace = True)
data["BedroomAbvGr"].replace(1, 2,inplace = True)
data["BedroomAbvGr"].replace(6, 2,inplace = True)
data["BedroomAbvGr"].replace(5, 3,inplace = True)
data["BedroomAbvGr"].replace(0, 4,inplace = True)


data.BedroomAbvGr.value_counts()

3    1161
2     583
4     329
Name: BedroomAbvGr, dtype: int64


data.BedroomAbvGr.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ BedroomAbvGr', data=data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq           F        PR(>F)
BedroomAbvGr     1.0  5.729822e+11  5.729822e+11  135.836339  1.890442e-30
Residual      2071.0  8.735852e+12  4.218181e+09         NaN           NaN


t_BedroomAbvGr = pairwise_tukeyhsd(data.Property_Sale_Price, data.BedroomAbvGr)
print(t_BedroomAbvGr)

    Multiple Comparison of Means - Tukey HSD, FWER=0.05    
===========================================================
group1 group2  meandiff  p-adj   lower      upper    reject
-----------------------------------------------------------
     2      3 24578.6284 0.001 16844.7854 32312.4713   True
     2      4 51365.0587 0.001 40859.0517 61871.0658   True
     3      4 26786.4303 0.001 17270.4931 36302.3676   True
-----------------------------------------------------------


sns.countplot(data.BedroomAbvGr)
plt.title('Count plot of BedroomAbvGr')

Text(0.5, 1.0, 'Count plot of BedroomAbvGr')


sns.catplot('BedroomAbvGr', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of BedroomAbvGr vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of BedroomAbvGr vs Property_Sale_Price')


sns.boxplot('BedroomAbvGr', 'Property_Sale_Price', data=data)
plt.title('Box Plot of BedroomAbvGr vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of BedroomAbvGr vs Property_Sale_Price')


data.KitchenAbvGr.isnull().sum()

0


data.KitchenAbvGr.describe()

count    2073.000000
mean        1.052581
std         0.237901
min         0.000000
25%         1.000000
50%         1.000000
75%         1.000000
max         3.000000
Name: KitchenAbvGr, dtype: float64


data.KitchenAbvGr.value_counts()

1    1965
2     101
3       5
0       2
Name: KitchenAbvGr, dtype: int64


data.KitchenAbvGr.value_counts().sum()

2073


sns.countplot(data.KitchenAbvGr)
plt.title('Count plot of KitchenAbvGr')

Text(0.5, 1.0, 'Count plot of KitchenAbvGr')


data.groupby("KitchenAbvGr")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ KitchenAbvGr', data=data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq          F        PR(>F)
KitchenAbvGr     1.0  2.335843e+11  2.335843e+11  53.304667  4.052531e-13
Residual      2071.0  9.075250e+12  4.382062e+09        NaN           NaN


t_KitchenAbvGr = pairwise_tukeyhsd(data.Property_Sale_Price, data.KitchenAbvGr)
print(t_KitchenAbvGr)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
     0      1  52690.1053 0.6532  -67628.4171 173008.6278  False
     0      2   3815.2574    0.9 -117626.9109 125257.4258  False
     0      3    -15900.0    0.9 -158190.4017 126390.4017  False
     1      2 -48874.8479  0.001  -66226.8575 -31522.8383   True
     1      3 -68590.1053 0.0947  -144744.228   7564.0173  False
     2      3 -19715.2574    0.9  -97632.5456  58202.0307  False
----------------------------------------------------------------


data["KitchenAbvGr"].replace(0, 2,inplace = True)
data["KitchenAbvGr"].replace(3, 2,inplace = True)


data.KitchenAbvGr.value_counts()

1    1965
2     108
Name: KitchenAbvGr, dtype: int64


data.KitchenAbvGr.value_counts().sum()

2073


df1=data[data.KitchenAbvGr==1]

df2=data[data.KitchenAbvGr==2]

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=7.6294265770566945, pvalue=3.571786832048337e-14)


sns.countplot(data.KitchenAbvGr)
plt.title('Count plot of KitchenAbvGr')

Text(0.5, 1.0, 'Count plot of KitchenAbvGr')


sns.catplot('KitchenAbvGr', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of KitchenAbvGr vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of KitchenAbvGr vs Property_Sale_Price')


sns.boxplot('KitchenAbvGr', 'Property_Sale_Price', data=data)
plt.title('Box Plot of KitchenAbvGr vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of KitchenAbvGr vs Property_Sale_Price')


data.KitchenQual.isnull().sum()

0


data.KitchenQual.value_counts()

TA    1043
Gd     838
Ex     144
Fa      48
Name: KitchenQual, dtype: int64


data.KitchenQual.value_counts().sum()

2073


sns.countplot(data.KitchenQual)
plt.title('Count plot of KitchenQual')

Text(0.5, 1.0, 'Count plot of KitchenQual')


data.groupby("KitchenQual")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ KitchenQual', data=data).fit()
print(sm.stats.anova_lm(mod))

                 df        sum_sq       mean_sq           F         PR(>F)
KitchenQual     3.0  4.453250e+12  1.484417e+12  632.520772  9.879853e-292
Residual     2069.0  4.855584e+12  2.346827e+09         NaN            NaN


t_KitchenQual = pairwise_tukeyhsd(data.Property_Sale_Price, data.KitchenQual)
print(t_KitchenQual)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj    lower        upper     reject
-----------------------------------------------------------------
    Ex     Fa -182238.6736 0.001 -202997.9522  -161479.395   True
    Ex     Gd  -76426.3086 0.001   -87662.419  -65190.1982   True
    Ex     TA   -148125.34 0.001 -159198.3443 -137052.3358   True
    Fa     Gd   105812.365 0.001   87326.5867  124298.1433   True
    Fa     TA   34113.3336 0.001    15726.239   52500.4282   True
    Gd     TA  -71699.0314 0.001   -77477.247  -65920.8158   True
-----------------------------------------------------------------


sns.catplot('KitchenQual', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of KitchenQual vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of KitchenQual vs Property_Sale_Price')


sns.boxplot('KitchenQual', 'Property_Sale_Price', data=data)
plt.title('Box Plot of KitchenQual vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of KitchenQual vs Property_Sale_Price')


data.TotRmsAbvGrd.isnull().sum()

0


data.TotRmsAbvGrd.describe()

count    2073.000000
mean        6.540280
std         1.629673
min         2.000000
25%         5.000000
50%         6.000000
75%         7.000000
max        14.000000
Name: TotRmsAbvGrd, dtype: float64


data.TotRmsAbvGrd.value_counts()

6     580
7     458
5     396
8     269
4     127
9     106
10     65
11     30
3      22
12     18
2       1
14      1
Name: TotRmsAbvGrd, dtype: int64


data.TotRmsAbvGrd.value_counts().sum()

2073


plt.hist(data.TotRmsAbvGrd)
plt.title('Histogram of TotRmsAbvGrd')

Text(0.5, 1.0, 'Histogram of TotRmsAbvGrd')


sns.distplot(data.TotRmsAbvGrd)
plt.title('Normality of TotRmsAbvGrd')

Text(0.5, 1.0, 'Normality of TotRmsAbvGrd')


sns.countplot(data.TotRmsAbvGrd)
plt.title('Count plot of TotRmsAbvGrd')

Text(0.5, 1.0, 'Count plot of TotRmsAbvGrd')


sns.boxplot(data.TotRmsAbvGrd, color = 'red')
plt.title('BoxPlot Of TotRmsAbvGrd')

Text(0.5, 1.0, 'BoxPlot Of TotRmsAbvGrd')


IQR = data.TotRmsAbvGrd.quantile(.75) - data.TotRmsAbvGrd.quantile(.25)
IQR

2.0


upper_limit = data.TotRmsAbvGrd.quantile(.75) + (1.5*IQR)
upper_limit

10.0


lower_limit = data.TotRmsAbvGrd.quantile(.25) - (1.5*IQR)
lower_limit

2.0


len(data.TotRmsAbvGrd[data.TotRmsAbvGrd  > upper_limit])

49


len(data.TotRmsAbvGrd[data.TotRmsAbvGrd  < lower_limit])

0


for i in np.arange(upper_limit, 12, 1):
  outliers = len(data.TotRmsAbvGrd[data.TotRmsAbvGrd  > i])
  print('At the point of:', i, 'There are ', outliers)

At the point of: 10.0 There are  49
At the point of: 11.0 There are  19


data.TotRmsAbvGrd = np.where(data.TotRmsAbvGrd > upper_limit, upper_limit, data.TotRmsAbvGrd)


sns.boxplot(data.TotRmsAbvGrd, color = 'g')
plt.title('BoxPlot Of TotRmsAbvGrd')

Text(0.5, 1.0, 'BoxPlot Of TotRmsAbvGrd')


np.corrcoef(data.TotRmsAbvGrd, data.Property_Sale_Price)

array([[1.       , 0.5244364],
       [0.5244364, 1.       ]])


sns.scatterplot(data.TotRmsAbvGrd,data.Property_Sale_Price )

<AxesSubplot:xlabel='TotRmsAbvGrd', ylabel='Property_Sale_Price'>


data.Functional.isnull().sum()

0


data.Functional.value_counts()

Typ     1937
Min1      49
Min2      47
Mod       18
Maj1      15
Maj2       6
Sev        1
Name: Functional, dtype: int64


data.Functional.value_counts().sum()

2073


sns.countplot(data.Functional)
plt.title('Count Plot of Functional - count')

Text(0.5, 1.0, 'Count Plot of Functional - count')


data.groupby("Functional")["Property_Sale_Price"].describe()


mod_GC = ols('Property_Sale_Price ~ Functional', data = data).fit()
sm.stats.anova_lm(mod_GC)


tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.Functional, alpha = 0.05)
print(tukey_GC)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
  Maj1   Maj2    -68351.6 0.3356 -163077.2325  26374.0325  False
  Maj1   Min1  -9856.0218    0.9  -67722.2851  48010.2415  False
  Maj1   Min2 -11001.6284    0.9  -69155.7376  47152.4808  False
  Maj1    Mod   8531.7333    0.9  -60025.6462  77089.1128  False
  Maj1    Sev -25018.2667    0.9 -227550.1882 177513.6548  False
  Maj1    Typ  25891.1252 0.7155  -24937.5263  76719.7767  False
  Maj2   Min1  58495.5782 0.3943  -26322.1822 143313.3386  False
  Maj2   Min2  57349.9716 0.4231  -27664.4299 142364.3732  False
  Maj2    Mod  76883.3333 0.1765  -15559.4183  169326.085  False
  Maj2    Sev  43333.3333    0.9 -168479.6202 255146.2868  False
  Maj2    Typ  94242.7252 0.0096   14061.0573 174424.3931   True
  Min1   Min2  -1145.6066    0.9  -41183.1819  38891.9687  False
  Min1    Mod  18387.7551    0.9  -35660.5687  72436.0789  False
  Min1    Sev -15162.2449    0.9 -213253.8556 182929.3658  False
  Min1    Typ  35747.1469 0.0039    7380.6378   64113.656   True
  Min2    Mod  19533.3617    0.9  -34823.0298  73889.7532  False
  Min2    Sev -14016.6383    0.9 -212192.5254 184159.2488  False
  Min2    Typ  36892.7535 0.0033    7943.5769  65841.9302   True
   Mod    Sev    -33550.0    0.9 -235024.3063 167924.3063  False
   Mod    Typ  17359.3918    0.9  -29076.2485  63795.0322  False
   Sev    Typ  50909.3918    0.9 -145241.9111 247060.6948  False
----------------------------------------------------------------


data["Functional"].replace('Maj1', 'Min1',inplace = True)
data["Functional"].replace('Min2', 'Min1',inplace = True)
data["Functional"].replace('Mod', 'Min1',inplace = True)
data["Functional"].replace('Sev', 'Min1',inplace = True)


data.Functional.value_counts()

Typ     1937
Min1     130
Maj2       6
Name: Functional, dtype: int64


data.Functional.value_counts().sum()

2073


mod_GC = ols('Property_Sale_Price ~ Functional', data = data).fit()
sm.stats.anova_lm(mod_GC)


tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.Functional, alpha = 0.05)
print(tukey_GC)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05     
=============================================================
group1 group2  meandiff  p-adj    lower       upper    reject
-------------------------------------------------------------
  Maj2   Min1 61647.9949 0.0676 -3388.6096 126684.5993  False
  Maj2    Typ 94242.7252 0.0015 30558.5291 157926.9212   True
  Min1    Typ 32594.7303  0.001 18483.3513  46706.1093   True
-------------------------------------------------------------


data["Functional"].replace('Maj2', 'Min1',inplace = True)


data.Functional.value_counts()

Typ     1937
Min1     136
Name: Functional, dtype: int64


data.Functional.value_counts().sum()

2073


df1=data[data.Functional=='Typ']

df2=data[data.Functional=='Min1']

ttest_ind(df1.Property_Sale_Price,df2.Property_Sale_Price)

Ttest_indResult(statistic=5.989060705579733, pvalue=2.4817905193362626e-09)


sns.catplot('Functional', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of Functional vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Functional vs Property_Sale_Price')


sns.boxplot('Functional', 'Property_Sale_Price', data=data)
plt.title('Box Plot of Functional vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Functional vs Property_Sale_Price')


data.Fireplaces.isnull().sum()

0


data.Fireplaces.describe()

count    2073.000000
mean        0.602026
std         0.633931
min         0.000000
25%         0.000000
50%         1.000000
75%         1.000000
max         3.000000
Name: Fireplaces, dtype: float64


data.Fireplaces.value_counts()

0    988
1    927
2    153
3      5
Name: Fireplaces, dtype: int64


data.Fireplaces.value_counts().sum()

2073


sns.countplot(data.Fireplaces)
plt.title('Count plot of Fireplaces')

Text(0.5, 1.0, 'Count plot of Fireplaces')


data.groupby("Fireplaces")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ Fireplaces', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F         PR(>F)
Fireplaces     1.0  2.216651e+12  2.216651e+12  647.287754  1.763214e-124
Residual    2071.0  7.092184e+12  3.424521e+09         NaN            NaN


t_Fireplaces = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fireplaces)
print(t_Fireplaces)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
     0      1  66722.9159  0.001  59965.5961  73480.2357   True
     0      2  79867.6968  0.001  67028.8191  92706.5744   True
     0      3 105393.3765  0.001  39138.2367 171648.5164   True
     1      2  13144.7809 0.0438    249.3838   26040.178   True
     1      3  38670.4606 0.4389 -27595.6547  104936.576  False
     2      3  25525.6797 0.7367 -41633.6342  92684.9937  False
---------------------------------------------------------------


data["Fireplaces"].replace(3, 2,inplace = True)


data.Fireplaces.value_counts()

0    988
1    927
2    158
Name: Fireplaces, dtype: int64


data.Fireplaces.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ Fireplaces', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F         PR(>F)
Fireplaces     1.0  2.231890e+12  2.231890e+12  653.141157  1.894439e-125
Residual    2071.0  7.076944e+12  3.417163e+09         NaN            NaN


t_Fireplaces = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fireplaces)
print(t_Fireplaces)

    Multiple Comparison of Means - Tukey HSD, FWER=0.05     
============================================================
group1 group2  meandiff  p-adj    lower      upper    reject
------------------------------------------------------------
     0      1 66722.9159  0.001 60558.8601 72886.9717   True
     0      2 80675.4715  0.001 69125.3699  92225.573   True
     1      2 13952.5556 0.0134  2350.1787 25554.9325   True
------------------------------------------------------------


sns.countplot(data.Fireplaces)
plt.title('Count plot of Fireplaces')

Text(0.5, 1.0, 'Count plot of Fireplaces')


sns.catplot('Fireplaces', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of Fireplaces vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of Fireplaces vs Property_Sale_Price')


sns.boxplot('Fireplaces', 'Property_Sale_Price', data=data)
plt.title('Box Plot of Fireplaces vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of Fireplaces vs Property_Sale_Price')


data.FireplaceQu.isnull().sum()

988


data.FireplaceQu.dtype

dtype('O')


data.FireplaceQu.fillna('No Fireplace' , inplace = True)


data.FireplaceQu.isnull().sum()

0


data.FireplaceQu.value_counts()

No Fireplace    988
Gd              530
TA              457
Fa               40
Ex               33
Po               25
Name: FireplaceQu, dtype: int64


data.FireplaceQu.value_counts().sum()

2073


sns.countplot(data.FireplaceQu)
plt.title('Count plot of FireplaceQu')

Text(0.5, 1.0, 'Count plot of FireplaceQu')


data.groupby("FireplaceQu")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ FireplaceQu', data=data).fit()
print(sm.stats.anova_lm(mod))

                 df        sum_sq       mean_sq           F         PR(>F)
FireplaceQu     5.0  2.936194e+12  5.872387e+11  190.474012  3.623749e-167
Residual     2067.0  6.372641e+12  3.083039e+09         NaN            NaN


t_FireplaceQu = pairwise_tukeyhsd(data.Property_Sale_Price, data.FireplaceQu)
print(t_FireplaceQu)

             Multiple Comparison of Means - Tukey HSD, FWER=0.05              
==============================================================================
   group1       group2      meandiff   p-adj     lower        upper     reject
------------------------------------------------------------------------------
          Ex           Fa -119133.4167  0.001 -156378.3621  -81888.4712   True
          Ex           Gd  -67024.2723  0.001  -95439.5674  -38608.9772   True
          Ex No Fireplace -144948.0432  0.001 -172974.6362 -116921.4502   True
          Ex           Po -154087.3467  0.001 -196080.6366 -112094.0567   True
          Ex           TA  -84309.3538  0.001 -112857.3643  -55761.3432   True
          Fa           Gd   52109.1443  0.001   26139.7169   78078.5718   True
          Fa No Fireplace  -25814.6265 0.0459  -51358.1596    -271.0934   True
          Fa           Po    -34953.93 0.1335  -75332.3719    5424.5119  False
          Fa           TA   34824.0629  0.002     8709.487   60938.6388   True
          Gd No Fireplace  -77923.7709  0.001  -86451.0869  -69396.4549   True
          Gd           Po  -87063.0743  0.001  -119476.985  -54649.1637   True
          Gd           TA  -17285.0814  0.001  -27395.1807   -7174.9822   True
No Fireplace           Po   -9139.3035    0.9   -41213.008   22934.4011  False
No Fireplace           TA   60638.6894  0.001   51679.0563   69598.3226   True
          Po           TA   69777.9929  0.001   37247.6761  102308.3097   True
------------------------------------------------------------------------------


data["FireplaceQu"].replace('Po', 'No Fireplace',inplace = True)


data.FireplaceQu.value_counts()

No Fireplace    1013
Gd               530
TA               457
Fa                40
Ex                33
Name: FireplaceQu, dtype: int64


data.FireplaceQu.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ FireplaceQu', data=data).fit()
print(sm.stats.anova_lm(mod))

                 df        sum_sq       mean_sq           F         PR(>F)
FireplaceQu     4.0  2.934157e+12  7.335392e+11  237.966422  3.065384e-168
Residual     2068.0  6.374677e+12  3.082533e+09         NaN            NaN


t_FireplaceQu = pairwise_tukeyhsd(data.Property_Sale_Price, data.FireplaceQu)
print(t_FireplaceQu)

             Multiple Comparison of Means - Tukey HSD, FWER=0.05             
=============================================================================
   group1       group2      meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------------------
          Ex           Fa -119133.4167  0.001 -154780.0274 -83486.8059   True
          Ex           Gd  -67024.2723  0.001  -94220.1495 -39828.3951   True
          Ex No Fireplace -145173.5936  0.001 -171986.7492 -118360.438   True
          Ex           TA  -84309.3538  0.001  -111632.251 -56986.4565   True
          Fa           Gd   52109.1443  0.001   27254.1724  76964.1162   True
          Fa No Fireplace  -26040.1769   0.03  -50475.7904  -1604.5635   True
          Fa           TA   34824.0629 0.0014    9830.1716  59817.9543   True
          Gd No Fireplace  -78149.3213  0.001  -86275.4573 -70023.1853   True
          Gd           TA  -17285.0814  0.001  -26961.3145  -7608.8484   True
No Fireplace           TA   60864.2399  0.001   52322.6321  69405.8476   True
-----------------------------------------------------------------------------


sns.countplot(data.FireplaceQu)
plt.title('Count plot of FireplaceQu')

Text(0.5, 1.0, 'Count plot of FireplaceQu')


sns.catplot('FireplaceQu', 'Property_Sale_Price', data=data)
plt.title('Cat Plot og FireplaceQu vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot og FireplaceQu vs Property_Sale_Price')


sns.boxplot('FireplaceQu', 'Property_Sale_Price', data=data)
plt.title('Box Plot og FireplaceQu vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot og FireplaceQu vs Property_Sale_Price')


data.GarageType.isnull().sum()

113


data.GarageType.dtype

dtype('O')


data.GarageType.fillna('No Garage', inplace = True)


data.GarageType.isnull().sum()

0


data.GarageType.value_counts()

Attchd       1252
Detchd        537
BuiltIn       124
No Garage     113
Basment        27
CarPort        12
2Types          8
Name: GarageType, dtype: int64


data.GarageType.value_counts().sum()

2073


sns.countplot(data.GarageType)
plt.title('Count plot of GarageType')

Text(0.5, 1.0, 'Count plot of GarageType')


data.groupby("GarageType")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ GarageType', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq          F         PR(>F)
GarageType     6.0  2.798865e+12  4.664775e+11  148.04104  1.756884e-156
Residual    2066.0  6.509969e+12  3.151001e+09        NaN            NaN


t_GarageType = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageType)
print(t_GarageType)

         Multiple Comparison of Means - Tukey HSD, FWER=0.05          
======================================================================
 group1   group2    meandiff   p-adj     lower        upper     reject
----------------------------------------------------------------------
 2Types    Attchd   48757.0703 0.1787  -10000.1608  107514.3013  False
 2Types   Basment    6324.2778    0.9  -60361.0597   73009.6153  False
 2Types   BuiltIn   91478.1129  0.001   31047.8705  151908.3553   True
 2Types   CarPort  -39740.9167 0.6874  -115354.982   35873.1486  False
 2Types    Detchd  -17489.6732    0.9   -76494.742   41515.3957  False
 2Types No Garage  -43306.7478 0.3481 -103914.9883   17301.4928  False
 Attchd   Basment  -42432.7925  0.002  -74656.4203  -10209.1647   True
 Attchd   BuiltIn   42721.0426  0.001   27124.8203   58317.2649   True
 Attchd   CarPort   -88497.987  0.001 -136549.1558  -40446.8181   True
 Attchd    Detchd  -66246.7435  0.001  -74792.2748  -57701.2122   True
 Attchd No Garage  -92063.8181  0.001 -108336.0882  -75791.5479   True
Basment   BuiltIn   85153.8351  0.001   49971.9622  120335.7081   True
Basment   CarPort  -46065.1944  0.214 -103540.7271   11410.3383  False
Basment    Detchd   -23813.951  0.323  -56487.3061    8859.4042  False
Basment No Garage  -49631.0256  0.001  -85117.7669  -14144.2842   True
BuiltIn   CarPort -131219.0296  0.001 -181302.1287  -81135.9304   True
BuiltIn    Detchd -108967.7861  0.001 -125473.1822    -92462.39   True
BuiltIn No Garage -134784.8607  0.001 -156329.9012 -113239.8202   True
CarPort    Detchd   22251.2435 0.8008  -26102.6682   70605.1552  False
CarPort No Garage   -3565.8311    0.9  -53863.5592   46731.8969  False
 Detchd No Garage  -25817.0746  0.001  -42962.7075   -8671.4417   True
----------------------------------------------------------------------


data["GarageType"].replace('2Types', 'Detchd',inplace = True)
data["GarageType"].replace('CarPort', 'No Garage',inplace = True)
data["GarageType"].replace('Basment', 'Detchd',inplace = True)


data.GarageType.value_counts()

Attchd       1252
Detchd        572
No Garage     125
BuiltIn       124
Name: GarageType, dtype: int64


data.GarageType.value_counts().sum()

2073


mod = ols('Property_Sale_Price ~ GarageType', data=data).fit()
print(sm.stats.anova_lm(mod))

                df        sum_sq       mean_sq           F         PR(>F)
GarageType     3.0  2.782040e+12  9.273466e+11  293.969762  6.068678e-159
Residual    2069.0  6.526794e+12  3.154565e+09         NaN            NaN


t_GarageType = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageType)
print(t_GarageType)

         Multiple Comparison of Means - Tukey HSD, FWER=0.05         
=====================================================================
 group1   group2    meandiff   p-adj    lower        upper     reject
---------------------------------------------------------------------
 Attchd   BuiltIn   42721.0426 0.001   29125.7384   56316.3468   True
 Attchd    Detchd  -64878.0476 0.001  -72165.9904  -57590.1047   True
 Attchd No Garage  -91721.4983 0.001 -105267.2315  -78175.7651   True
BuiltIn    Detchd -107599.0902 0.001 -121904.1106  -93294.0698   True
BuiltIn No Garage -134442.5409 0.001 -152745.7206 -116139.3612   True
 Detchd No Garage  -26843.4507 0.001  -41101.3679  -12585.5336   True
---------------------------------------------------------------------


sns.countplot(data.GarageType)
plt.title('Count plot of GarageType')

Text(0.5, 1.0, 'Count plot of GarageType')


sns.catplot('GarageType', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of GarageType vs Property_Sale_Price')

Text(0.5, 1.0, 'Cat Plot of GarageType vs Property_Sale_Price')


sns.boxplot('GarageType', 'Property_Sale_Price', data=data)
plt.title('Box Plot of GarageType vs Property_Sale_Price')

Text(0.5, 1.0, 'Box Plot of GarageType vs Property_Sale_Price')


data.GarageYrBlt.isnull().sum()

113


data.GarageYrBlt.dtype

dtype('float64')


data.GarageYrBlt.sample(10)

1445    1990.0
1007    1970.0
291        NaN
1771    1995.0
2003    1960.0
370     2000.0
1736    2007.0
325     1941.0
175     1950.0
549     2003.0
Name: GarageYrBlt, dtype: float64


data.GarageYrBlt.median()

1984.0


data.GarageYrBlt.fillna(data.GarageYrBlt.median(), inplace = True)


data.GarageYrBlt.isnull().sum()

0


data.GarageYrBlt.describe()

count    2073.000000
mean     1980.613603
std        23.504410
min      1906.000000
25%      1964.000000
50%      1984.000000
75%      2003.000000
max      2019.000000
Name: GarageYrBlt, dtype: float64


data.GarageYrBlt.value_counts()

1984.0    122
2005.0    116
2006.0     82
2007.0     81
2003.0     72
         ... 
2015.0      1
2019.0      1
1908.0      1
1906.0      1
2013.0      1
Name: GarageYrBlt, Length: 102, dtype: int64


data.GarageYrBlt.value_counts().sum()

2073


plt.hist(data.GarageYrBlt)
plt.title('Histogram of GarageYrBlt')

Text(0.5, 1.0, 'Histogram of GarageYrBlt')


sns.distplot(data.GarageYrBlt)
plt.title('Normality of GarageYrBlt')

Text(0.5, 1.0, 'Normality of GarageYrBlt')


sns.countplot(data.GarageYrBlt)
plt.title('Count plot of GarageYrBlt')

Text(0.5, 1.0, 'Count plot of GarageYrBlt')


sns.boxplot(data.GarageYrBlt, color = 'g')
plt.title('BoxPlot Of GarageYrBlt')

Text(0.5, 1.0, 'BoxPlot Of GarageYrBlt')


np.corrcoef(data.Property_Sale_Price, data.GarageYrBlt)

array([[1.        , 0.47316836],
       [0.47316836, 1.        ]])


sns.scatterplot(data.Property_Sale_Price, data.GarageYrBlt)
plt.title('GarageYrBlt vs Property_Sale_Price')

Text(0.5, 1.0, 'GarageYrBlt vs Property_Sale_Price')


data.GarageFinish.isnull().sum()

113


data.GarageFinish.dtype

dtype('O')


data.GarageFinish.fillna('No Garage', inplace = True)


data.GarageFinish.isnull().sum()

0


data.GarageFinish.value_counts()

Unf          857
RFn          605
Fin          498
No Garage    113
Name: GarageFinish, dtype: int64


data.GarageFinish.value_counts().sum()

2073


sns.countplot(data.GarageFinish)
plt.title('Count plot of GarageFinish')

Text(0.5, 1.0, 'Count plot of GarageFinish')


data.groupby("GarageFinish")["Property_Sale_Price"].describe()


mod = ols('Property_Sale_Price ~ GarageFinish', data=data).fit()
print(sm.stats.anova_lm(mod))

                  df        sum_sq       mean_sq          F         PR(>F)
GarageFinish     3.0  3.372449e+12  1.124150e+12  391.79839  1.683598e-201
Residual      2069.0  5.936385e+12  2.869205e+09        NaN            NaN


t_GarageFinish = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageFinish)
print(t_GarageFinish)

          Multiple Comparison of Means - Tukey HSD, FWER=0.05          
=======================================================================
  group1    group2    meandiff   p-adj    lower        upper     reject
-----------------------------------------------------------------------
      Fin No Garage -123146.4546 0.001 -137497.0688 -108795.8405   True
      Fin       RFn  -29525.2498 0.001  -37858.2074  -21192.2922   True
      Fin       Unf  -88604.4355 0.001  -96364.5538  -80844.3173   True
No Garage       RFn   93621.2048 0.001   79507.2381  107735.1715   True
No Garage       Unf   34542.0191 0.001   20758.5032   48325.5349   True
      RFn       Unf  -59079.1857 0.001  -66392.4179  -51765.9535   True
-----------------------------------------------------------------------


sns.catplot('GarageFinish', 'Property_Sale_Price', data=data)
plt.title('Cat Plot of GarageType vs GarageFinish')

Text(0.5, 1.0, 'Cat Plot of GarageType vs GarageFinish')


sns.boxplot('GarageFinish', 'Property_Sale_Price', data=data)
plt.title('Box Plot of GarageType vs GarageFinish')

Text(0.5, 1.0, 'Box Plot of GarageType vs GarageFinish')


data.GarageCars.isnull().sum()

0


data.GarageCars.describe()

count    2073.000000
mean        1.775687
std         0.741976
min         0.000000
25%         1.000000
50%         2.000000
75%         2.000000
max         4.000000
Name: GarageCars, dtype: float64


data.GarageCars.value_counts()

2    1185
1     510
3     259
0     113
4       6
Name: GarageCars, dtype: int64


data.GarageCars.value_counts().sum()

2073


sns.countplot(data.GarageCars)
plt.title('GarageCars - count')

Text(0.5, 1.0, 'GarageCars - count')


data.groupby("GarageCars")["Property_Sale_Price"].describe()


mod_GC = ols('Property_Sale_Price ~ GarageCars', data = data).fit()
sm.stats.anova_lm(mod_GC)


tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageCars, alpha = 0.05)
print(tukey_GC)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05       
================================================================
group1 group2   meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------
     0      1  21822.3458  0.001    8305.1442  35339.5475   True
     0      2  76494.2706  0.001   63694.3867  89294.1544   True
     0      3 174534.1435  0.001  159876.9965 189191.2906   True
     0      4  98470.5811  0.001   44004.5521 152936.6102   True
     1      2  54671.9247  0.001   47786.8657  61556.9838   True
     1      3 152711.7977  0.001  142792.1721 162631.4233   True
     1      4  76648.2353  0.001   23261.7639 130034.7067   True
     2      3   98039.873  0.001   89122.4037 106957.3422   True
     2      4  21976.3105 0.7654  -31233.0647  75185.6858  False
     3      4 -76063.5624 0.0011 -129749.9904 -22377.1344   True
----------------------------------------------------------------


data["GarageCars"].replace(4,2,inplace = True)


data.GarageCars.value_counts()

2    1191
1     510
3     259
0     113
Name: GarageCars, dtype: int64


data.GarageCars.value_counts().sum()

2073


mod_GC = ols('Property_Sale_Price ~ GarageCars', data = data).fit()
sm.stats.anova_lm(mod_GC)


tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageCars, alpha = 0.05)
print(tukey_GC)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1 group2   meandiff  p-adj    lower       upper    reject
--------------------------------------------------------------
     0      1  21822.3458 0.001   9091.7614  34552.9302   True
     0      2  76604.9825 0.001  64552.6162  88657.3487   True
     0      3 174534.1435 0.001 160729.9515 188338.3356   True
     1      2  54782.6366 0.001  48303.1617  61262.1116   True
     1      3 152711.7977 0.001 143369.4328 162054.1626   True
     2      3  97929.1611 0.001  89534.4285 106323.8936   True
--------------------------------------------------------------


sns.countplot(x = 'GarageCars', data = data)
plt.title('GarageCars - count')

Text(0.5, 1.0, 'GarageCars - count')


sns.catplot(x = 'GarageCars', y = 'Property_Sale_Price',data = data)
plt.title('Catplot Of GarageCars vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of GarageCars vs Property_Sale_Price')


sns.boxplot(x = 'GarageCars', y = 'Property_Sale_Price',data = data)
plt.title('Boxplot Of GarageCars vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot Of GarageCars vs Property_Sale_Price')


data.GarageArea.isnull().sum()

0


data.GarageArea.describe()

count    2073.000000
mean      474.683068
std       212.284955
min         0.000000
25%       336.000000
50%       480.000000
75%       576.000000
max      1418.000000
Name: GarageArea, dtype: float64


data.GarageArea.value_counts()

0      113
440     72
576     61
240     54
528     53
      ... 
350      1
342      1
283      1
292      1
770      1
Name: GarageArea, Length: 441, dtype: int64


data.GarageArea.value_counts().sum()

2073


sns.displot(data.GarageArea)
plt.title('Histogram Of GarageArea')

Text(0.5, 1.0, 'Histogram Of GarageArea')


sns.distplot(data.GarageArea)
plt.title('Normality Of GarageArea')

Text(0.5, 1.0, 'Normality Of GarageArea')


sns.countplot(data.GarageArea)
plt.title('GarageArea - count')

Text(0.5, 1.0, 'GarageArea - count')


sns.boxplot(data.GarageArea, color = 'red')
plt.title('BoxPlot Of GarageArea')

Text(0.5, 1.0, 'BoxPlot Of GarageArea')


IQR = data.GarageArea.quantile(0.75) - data.GarageArea.quantile(0.25)
IQR

240.0


UL = data.GarageArea.quantile(0.75) + (1.5*IQR)
UL

936.0


LL = data.GarageArea.quantile(0.25) - (1.5*IQR)
LL

-24.0


data.GarageArea[(data.GarageArea > UL)].value_counts().sum()

27


data.GarageArea[(data.GarageArea < LL)].value_counts().sum()

0


data['GarageArea'] = np.where(data['GarageArea'] > UL, UL, data['GarageArea'])


sns.boxplot(data.GarageArea, color = 'green')
plt.title('BoxPlot Of GarageArea')

Text(0.5, 1.0, 'BoxPlot Of GarageArea')


sns.distplot(data.GarageArea)
plt.title('Normality Of GarageArea')

Text(0.5, 1.0, 'Normality Of GarageArea')


np.corrcoef(data.GarageArea, data.Property_Sale_Price)

array([[1.        , 0.65435751],
       [0.65435751, 1.        ]])


sns.scatterplot(x = data['GarageArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('GarageArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of GarageArea vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter Plot Of GarageArea vs Property_Sale_Price')


data.GarageQual.isnull().sum()

113


data.GarageQual.dtype

dtype('O')


data['GarageQual'] = data['GarageQual'].fillna('No Garage')


data.GarageQual.isnull().sum()

0


data.GarageQual.value_counts()

TA           1870
No Garage     113
Fa             65
Gd             18
Ex              4
Po              3
Name: GarageQual, dtype: int64


data.GarageQual.value_counts().sum()

2073


sns.countplot(data.GarageQual)
plt.title('GarageQual - count')

Text(0.5, 1.0, 'GarageQual - count')


data.groupby("GarageQual")["Property_Sale_Price"].describe()


mod_GQ = ols('Property_Sale_Price ~ GarageQual', data = data).fit()
sm.stats.anova_lm(mod_GQ)


tukey_GQ = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageQual, alpha = 0.05)
print(tukey_GQ)

          Multiple Comparison of Means - Tukey HSD, FWER=0.05          
=======================================================================
  group1    group2    meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------------
       Ex        Fa  -57365.4462 0.5006 -151074.8873   36343.995  False
       Ex        Gd   46903.8889 0.7405  -53648.0777 147455.8555  False
       Ex No Garage  -72469.2478 0.2228 -165017.7064  20079.2108  False
       Ex        Po  -78708.3333 0.5758 -217640.8407   60224.174  False
       Ex        TA    4626.2872    0.9  -86423.6124  95676.1867  False
       Fa        Gd   104269.335  0.001   55819.5793 152719.0908   True
       Fa No Garage  -15103.8016 0.6304   -43421.606  13214.0028  False
       Fa        Po  -21342.8872    0.9 -128762.2637  86076.4893  False
       Fa        TA   61991.7333  0.001   39040.3704  84943.0962   True
       Gd No Garage -119373.1367  0.001 -165537.3724 -73208.9009   True
       Gd        Po -125612.2222   0.02 -239050.1394  -12174.305   True
       Gd        TA  -42277.6017  0.058  -85358.9628    803.7594  False
No Garage        Po   -6239.0855    0.9 -112647.1693 100168.9982  False
No Garage        TA    77095.535  0.001   59473.8753  94717.1946   True
       Po        TA   83334.6205 0.2105  -21772.6927 188441.9337  False
-----------------------------------------------------------------------


data["GarageQual"].replace('Ex','TA',inplace = True)
data["GarageQual"].replace('Po','Fa',inplace = True)


data.GarageQual.value_counts()

TA           1874
No Garage     113
Fa             68
Gd             18
Name: GarageQual, dtype: int64


data.GarageQual.value_counts().sum()

2073


mod_GQ = ols('Property_Sale_Price ~ GarageQual', data = data).fit()
sm.stats.anova_lm(mod_GQ)


tukey_GQ = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageQual, alpha = 0.05)
print(tukey_GQ)

         Multiple Comparison of Means - Tukey HSD, FWER=0.05          
======================================================================
  group1    group2    meandiff   p-adj     lower       upper    reject
----------------------------------------------------------------------
       Fa        Gd   105210.933  0.001  61764.9599 148656.9062   True
       Fa No Garage  -14162.2037 0.4708 -39317.9161  10993.5088  False
       Fa        TA   62923.4566  0.001  42689.7068  83157.2064   True
       Gd No Garage -119373.1367  0.001   -160969.1 -77777.1734   True
       Gd        TA  -42287.4764 0.0264  -81105.242  -3469.7108   True
No Garage        TA   77085.6603  0.001  61208.7522  92962.5684   True
----------------------------------------------------------------------


data["GarageQual"].replace('Fa', 'No Garage',inplace = True)


data.GarageQual.value_counts()

TA           1874
No Garage     181
Gd             18
Name: GarageQual, dtype: int64


data.GarageQual.value_counts().sum()

2073


mod_GQ = ols('Property_Sale_Price ~ GarageQual', data = data).fit()
sm.stats.anova_lm(mod_GQ)


tukey_GQ = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageQual, alpha = 0.05)
print(tukey_GQ)

          Multiple Comparison of Means - Tukey HSD, FWER=0.05          
=======================================================================
  group1    group2    meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------------
       Gd No Garage -114052.5298  0.001 -151014.3784 -77090.6812   True
       Gd        TA  -42287.4764 0.0143  -77706.9584  -6867.9944   True
No Garage        TA   71765.0534  0.001   60124.2081  83405.8987   True
-----------------------------------------------------------------------


sns.countplot(x = 'GarageQual', data = data)
plt.title('GarageQual - count')

Text(0.5, 1.0, 'GarageQual - count')


sns.catplot(x = 'GarageQual', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of GarageQual vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of GarageQual vs Property_Sale_Price')


sns.boxplot(x = 'GarageQual', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of GarageQual vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot Of GarageQual vs Property_Sale_Price')


data.GarageCond.isnull().sum()

113


data.GarageCond.dtype

dtype('O')


data['GarageCond'] = data['GarageCond'].fillna('No Garage')


data.GarageCond.value_counts().sum()

2073


data.GarageCond.value_counts()

TA           1885
No Garage     113
Fa             47
Gd             17
Po              8
Ex              3
Name: GarageCond, dtype: int64


data.GarageCond.value_counts().sum()

2073


sns.countplot(data.GarageCond)
plt.title('GarageCond - count')

Text(0.5, 1.0, 'GarageCond - count')


data.groupby("GarageCond")["Property_Sale_Price"].describe()


mod_GC = ols('Property_Sale_Price ~ GarageCond', data = data).fit()
sm.stats.anova_lm(mod_GC)


tukey_GC = pairwise_tukeyhsd(data.Property_Sale_Price, data.GarageCond, alpha = 0.05)
print(tukey_GC)

         Multiple Comparison of Means - Tukey HSD, FWER=0.05          
======================================================================
  group1    group2    meandiff  p-adj     lower        upper    reject
----------------------------------------------------------------------
       Ex        Fa -15945.5816    0.9 -124202.6326  92311.4695  False
       Ex        Gd  61941.5686 0.6127   -51902.565 175785.7023  False
       Ex No Garage -18760.9145    0.9 -125104.1555  87582.3266  False
       Ex        Po -15104.1667    0.9 -138179.6269 107971.2936  False
       Ex        TA  58682.3079 0.5887  -46360.2859 163724.9016  False
       Fa        Gd  77887.1502  0.001   26435.7327 129338.5677   True
       Fa No Garage  -2815.3329    0.9  -34369.1997  28738.5339  False
       Fa        Po    841.4149    0.9   -68687.935  70370.7648  False
       Fa        TA  74627.8894  0.001   47781.8823 101473.8966   True
       Gd No Garage -80702.4831  0.001 -127994.5985 -33410.3677   True
       Gd        Po -77045.7353 0.0548 -154989.4853    898.0148  False
       Gd        TA  -3259.2608    0.9  -47549.2796  41030.7581  False
No Garage        Po   3656.7478    0.9  -62853.5997  70167.0952  False
No Garage        TA  77443.2223  0.001   59836.2943  95050.1504   True
       Po        TA  73786.4745  0.014    9376.1655 138196.7836   True
----------------------------------------------------------------------


data["GarageCond"].replace('Fa','No Garage',inplace = True)
data["GarageCond"].replace('Ex','No Garage',inplace = True)
data["GarageCond"].replace('Po','No Garage',inplace = True)
data["GarageCond"].replace('Gd','TA',inplace = True)


data.GarageCond.value_counts()

TA           1902
No Garage     171
Name: GarageCond, dtype: int64


data.GarageCond.value_counts().sum()

2073


GC_TA = data[data.GarageCond == 'TA']
GC_NG = data[data.GarageCond == 'No Garage']
ttest_ind(GC_TA.Property_Sale_Price, GC_NG.Property_Sale_Price)

Ttest_indResult(statistic=14.988371426457498, pvalue=2.7101983787665408e-48)


sns.countplot(x = 'GarageCond', data = data)
plt.title('GarageCond - count')

Text(0.5, 1.0, 'GarageCond - count')


sns.catplot(x = 'GarageCond', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of GarageCond vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of GarageCond vs Property_Sale_Price')


sns.boxplot(x = 'GarageCond', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of GarageCond vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot Of GarageCond vs Property_Sale_Price')


data.PavedDrive.isnull().sum()

0


data.PavedDrive.value_counts()

Y    1913
N     116
P      44
Name: PavedDrive, dtype: int64


data.PavedDrive.value_counts().sum()

2073


sns.countplot(data.PavedDrive)
plt.title('PavedDrive - count')

Text(0.5, 1.0, 'PavedDrive - count')


data.groupby("PavedDrive")["Property_Sale_Price"].describe()


mod_PD = ols('Property_Sale_Price ~ PavedDrive', data = data).fit()
sm.stats.anova_lm(mod_PD)


tukey_PD = pairwise_tukeyhsd(data.Property_Sale_Price, data.PavedDrive, alpha = 0.05)
print(tukey_PD)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05     
=============================================================
group1 group2  meandiff  p-adj     lower      upper    reject
-------------------------------------------------------------
     N      P 15060.5408 0.3948 -12035.4007 42156.4822  False
     N      Y 61659.5507  0.001  47025.8512 76293.2501   True
     P      Y 46599.0099  0.001  23263.8288  69934.191   True
-------------------------------------------------------------


data["PavedDrive"].replace('P', 'N',inplace = True)


data.PavedDrive.value_counts()

Y    1913
N     160
Name: PavedDrive, dtype: int64


data.PavedDrive.value_counts().sum()

2073


PD_Y = data[data.PavedDrive == 'Y']
PD_N = data[data.PavedDrive == "N"]
ttest_ind(PD_Y.Property_Sale_Price, PD_N.Property_Sale_Price)

Ttest_indResult(statistic=10.709464579232613, pvalue=4.366284091297098e-26)


sns.countplot(x = 'PavedDrive', data = data)
plt.title('PavedDrive - count')

Text(0.5, 1.0, 'PavedDrive - count')


sns.catplot(x = 'PavedDrive', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of PavedDrive vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of PavedDrive vs Property_Sale_Price')


sns.boxplot(x = 'PavedDrive', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of PavedDrive vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot Of PavedDrive vs Property_Sale_Price')


data.WoodDeckSF.isnull().sum()

0


data.WoodDeckSF.describe()

count    2073.000000
mean       94.168837
std       129.072113
min         0.000000
25%         0.000000
50%         0.000000
75%       168.000000
max       857.000000
Name: WoodDeckSF, dtype: float64


data.WoodDeckSF.value_counts()

0      1089
192      51
100      51
144      45
168      43
       ... 
35        1
45        1
126       1
362       1
476       1
Name: WoodDeckSF, Length: 274, dtype: int64


data.WoodDeckSF.value_counts().sum()

2073


sns.displot(data.WoodDeckSF)
plt.title('Histogram Of WoodDeckSF')

Text(0.5, 1.0, 'Histogram Of WoodDeckSF')


sns.distplot(data.WoodDeckSF)
plt.title('Normality Of WoodDeckSF')

Text(0.5, 1.0, 'Normality Of WoodDeckSF')


sns.countplot(data.WoodDeckSF)
plt.title('WoodDeckSF - count')

Text(0.5, 1.0, 'WoodDeckSF - count')


sns.boxplot(data.WoodDeckSF, color = 'red')
plt.title('BoxPlot Of WoodDeckSF')

Text(0.5, 1.0, 'BoxPlot Of WoodDeckSF')


IQR = data.WoodDeckSF.quantile(0.75) - data.WoodDeckSF.quantile(0.25)
IQR

168.0


UL = data.WoodDeckSF.quantile(0.75) + (1.5*IQR)
UL

420.0


LL = data.WoodDeckSF.quantile(0.25) - (1.5*IQR)
LL

-252.0


data.WoodDeckSF[(data.WoodDeckSF > UL)].value_counts().sum()

50


data.WoodDeckSF[(data.WoodDeckSF < LL)].value_counts().sum()

0


data['WoodDeckSF'] = np.where(data['WoodDeckSF'] > UL, UL, data['WoodDeckSF'])


sns.boxplot(data.WoodDeckSF, color = 'green')
plt.title('BoxPlot Of WoodDeckSF')

Text(0.5, 1.0, 'BoxPlot Of WoodDeckSF')


sns.distplot(data.WoodDeckSF)
plt.title('Normality Of WoodDeckSF')

Text(0.5, 1.0, 'Normality Of WoodDeckSF')


np.corrcoef(data.WoodDeckSF, data.Property_Sale_Price)

array([[1.        , 0.34254742],
       [0.34254742, 1.        ]])


data.OpenPorchSF.isnull().sum()

0


data.OpenPorchSF.describe()

count    2073.000000
mean       46.035697
std        64.768713
min         0.000000
25%         0.000000
50%        24.000000
75%        67.000000
max       547.000000
Name: OpenPorchSF, dtype: float64


data.OpenPorchSF.value_counts()

0      939
36      42
20      31
48      30
40      29
      ... 
119      1
123      1
15       1
129      1
547      1
Name: OpenPorchSF, Length: 202, dtype: int64


data.OpenPorchSF.value_counts().sum()

2073


sns.displot(data.OpenPorchSF)
plt.title('Histogram Of OpenPorchSF')

Text(0.5, 1.0, 'Histogram Of OpenPorchSF')


sns.distplot(data.OpenPorchSF)
plt.title('Normality Of OpenPorchSF')

Text(0.5, 1.0, 'Normality Of OpenPorchSF')


sns.countplot(data.OpenPorchSF)
plt.title('OpenPorchSF - count')

Text(0.5, 1.0, 'OpenPorchSF - count')


sns.boxplot(data.OpenPorchSF, color = 'red')
plt.title('BoxPlot Of OpenPorchSF')

Text(0.5, 1.0, 'BoxPlot Of OpenPorchSF')


IQR = data.OpenPorchSF.quantile(0.75) - data.OpenPorchSF.quantile(0.25)
IQR

67.0


UL = data.OpenPorchSF.quantile(0.75) + (1.5*IQR)
UL

167.5


LL = data.OpenPorchSF.quantile(0.25) - (1.5*IQR)
LL

-100.5


data.OpenPorchSF[(data.OpenPorchSF > UL)].value_counts().sum()

118


data.OpenPorchSF[(data.OpenPorchSF < LL)].value_counts().sum()

0


data['OpenPorchSF'] = np.where(data['OpenPorchSF'] > UL, UL, data['OpenPorchSF'])


sns.boxplot(data.OpenPorchSF, color = 'green')
plt.title('BoxPlot Of OpenPorchSF')

Text(0.5, 1.0, 'BoxPlot Of OpenPorchSF')


sns.distplot(data.OpenPorchSF)
plt.title('Normality Of OpenPorchSF')

Text(0.5, 1.0, 'Normality Of OpenPorchSF')


np.corrcoef(data.OpenPorchSF, data.Property_Sale_Price)

array([[1.        , 0.42627987],
       [0.42627987, 1.        ]])


data.EnclosedPorch.isnull().sum()

0


data.EnclosedPorch.describe()

count    2073.000000
mean       21.029908
std        59.569928
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max       552.000000
Name: EnclosedPorch, dtype: float64


data.EnclosedPorch.value_counts()

0      1790
112      23
216       8
192       7
154       7
       ... 
248       1
254       1
268       1
272       1
301       1
Name: EnclosedPorch, Length: 120, dtype: int64


data.EnclosedPorch.value_counts().sum()

2073


sns.displot(data.EnclosedPorch)
plt.title('Histogram Of EnclosedPorch')

Text(0.5, 1.0, 'Histogram Of EnclosedPorch')


sns.distplot(data.EnclosedPorch)
plt.title('Normality Of EnclosedPorch')

Text(0.5, 1.0, 'Normality Of EnclosedPorch')


sns.countplot(data.EnclosedPorch)
plt.title('EnclosedPorch - count')

Text(0.5, 1.0, 'EnclosedPorch - count')


sns.boxplot(data.EnclosedPorch, color = 'red')
plt.title('BoxPlot Of EnclosedPorch')

Text(0.5, 1.0, 'BoxPlot Of EnclosedPorch')


IQR = data.EnclosedPorch.quantile(0.75) - data.EnclosedPorch.quantile(0.25)
IQR

0.0


UL = data.EnclosedPorch.quantile(0.75) + (1.5*IQR)
UL

0.0


LL = data.EnclosedPorch.quantile(0.25) - (1.5*IQR)
LL

0.0


np.corrcoef(data.EnclosedPorch, data.Property_Sale_Price)

array([[ 1.        , -0.11479018],
       [-0.11479018,  1.        ]])


mod_EP = ols('Property_Sale_Price ~ EnclosedPorch', data = data).fit()
sm.stats.anova_lm(mod_EP)


tukey_EP = pairwise_tukeyhsd(data.Property_Sale_Price, data.EnclosedPorch, alpha = 0.05)
print(tukey_EP)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
     0     19   37657.3642    0.9 -165235.9802 240550.7087  False
     0     20  -60342.6358    0.9 -347197.0841 226511.8126  False
     0     24 -100342.6358    0.9 -387197.0841 186511.8126  False
     0     30  -78342.6358    0.9 -365197.0841 208511.8126  False
     0     32  -89167.6358    0.9  -232714.933  54379.6615  False
     0     34  -63667.6358    0.9 -266560.9802 139225.7087  False
     0     36  -32892.6358    0.9 -235785.9802 170000.7087  False
     0     37  157657.3642    0.9 -129197.0841 444511.8126  False
     0     39   -1592.6358    0.9 -204485.9802 201300.7087  False
     0     40  -21225.9691    0.9 -186933.9072 144481.9691  False
     0     42  -13342.6358    0.9 -300197.0841 273511.8126  False
     0     44  -18842.6358    0.9 -221735.9802 184050.7087  False
     0     48  -91342.6358    0.9 -294235.9802 111550.7087  False
     0     50  -22342.6358    0.9 -225235.9802 180550.7087  False
     0     52   -3142.6358    0.9 -289997.0841 283711.8126  False
     0     54  -49342.6358    0.9 -336197.0841 237511.8126  False
     0     56  -22908.6358    0.9 -309763.0841 263945.8126  False
     0     60  -75342.6358    0.9 -362197.0841 211511.8126  False
     0     64  -42342.6358    0.9 -329197.0841 244511.8126  False
     0     67  -44842.6358    0.9 -331697.0841 242011.8126  False
     0     68  -24842.6358    0.9 -311697.0841 262011.8126  False
     0     70   -2342.6358    0.9 -205235.9802 200550.7087  False
     0     77 -100675.9691    0.9 -266383.9072  65031.9691  False
     0     80  -72192.6358    0.9 -275085.9802 130700.7087  False
     0     81  -43342.6358    0.9 -330197.0841 243511.8126  False
     0     84  -55842.6358    0.9 -258735.9802 147050.7087  False
     0     87 -113842.6358    0.9 -400697.0841 173011.8126  False
     0     90  -95342.6358    0.9 -382197.0841 191511.8126  False
     0     91 -106342.6358    0.9 -393197.0841 180511.8126  False
     0     94  -96342.6358    0.9 -299235.9802 106550.7087  False
     0     96  -80675.9691    0.9 -197947.1604  36595.2222  False
     0     98  -79566.6358    0.9 -366421.0841 207287.8126  False
     0     99     557.3642    0.9 -127871.0205  128985.749  False
     0    100 -121342.6358    0.9 -324235.9802  81550.7087  False
     0    102  -61442.6358    0.9 -178713.8271  55828.5556  False
     0    105  -63342.6358    0.9 -350197.0841 223511.8126  False
     0    108  -45675.9691    0.9 -211383.9072 120031.9691  False
     0    112  -60875.0271 0.0413 -121054.5555   -695.4986   True
     0    114  -39342.6358    0.9 -167771.0205   89085.749  False
     0    115 -101392.6358    0.9 -304285.9802 101500.7087  False
     0    116  -57717.6358    0.9  -201264.933  85829.6615  False
     0    120  -52442.6358    0.9 -180871.0205   75985.749  False
     0    123  -27342.6358    0.9 -314197.0841 259511.8126  False
     0    126  -39842.6358    0.9 -205550.5739 125865.3024  False
     0    128  -73467.6358    0.9  -217014.933  70079.6615  False
     0    129  -93342.6358    0.9 -380197.0841 193511.8126  False
     0    130  -67342.6358    0.9 -354197.0841 219511.8126  False
     0    134  -54342.6358    0.9 -257235.9802 148550.7087  False
     0    136  -54342.6358    0.9 -341197.0841 232511.8126  False
     0    137  -58342.6358    0.9 -261235.9802 144550.7087  False
     0    138  -80342.6358    0.9 -283235.9802 122550.7087  False
     0    140  -82342.6358    0.9 -369197.0841 204511.8126  False
     0    143  -22842.6358    0.9 -225735.9802 180050.7087  False
     0    144  -45914.0643    0.9 -154516.3122  62688.1836  False
     0    145    7657.3642    0.9 -279197.0841 294511.8126  False
     0    148  -74842.6358    0.9 -361697.0841 212011.8126  False
     0    150  -18642.6358    0.9 -147071.0205  109785.749  False
     0    154  -22942.6358    0.9 -131544.8837  85659.6122  False
     0    156  -37342.6358    0.9 -145944.8837  71259.6122  False
     0    158  -37875.9691    0.9 -203583.9072 127831.9691  False
     0    160  -53342.6358    0.9 -219050.5739 112365.3024  False
     0    162  152657.3642 0.1661  -13050.5739 318365.3024  False
     0    164  -74862.6358    0.9 -203291.0205   53565.749  False
     0    168  -54175.9691    0.9 -219883.9072 111531.9691  False
     0    169   -7342.6358    0.9 -294197.0841 279511.8126  False
     0    170   31157.3642    0.9 -171735.9802 234050.7087  False
     0    172 -142342.6358    0.9 -429197.0841 144511.8126  False
     0    174  104657.3642    0.9  -98235.9802 307550.7087  False
     0    176  -59709.3024    0.9 -225417.2406 105998.6357  False
     0    177   16157.3642    0.9 -270697.0841 303011.8126  False
     0    180  -23592.6358    0.9 -226485.9802 179300.7087  False
     0    183  -30342.6358    0.9 -317197.0841 256511.8126  False
     0    184  -55009.3024    0.9 -220717.2406 110698.6357  False
     0    185  -33342.6358    0.9  -176889.933 110204.6615  False
     0    189  -29342.6358    0.9 -316197.0841 257511.8126  False
     0    190  -73092.6358    0.9 -275985.9802 129800.7087  False
     0    192   -4842.6358    0.9 -113444.8837 103759.6122  False
     0    194  -28442.6358    0.9 -315297.0841 258411.8126  False
     0    196  -43342.6358    0.9 -246235.9802 159550.7087  False
     0    198  -77342.6358    0.9 -364197.0841 209511.8126  False
     0    200   18281.3642    0.9 -268573.0841 305135.8126  False
     0    202   41357.3642    0.9 -161535.9802 244250.7087  False
     0    205  -46109.3024    0.9 -211817.2406 119598.6357  False
     0    208  -65342.6358    0.9 -352197.0841 221511.8126  False
     0    210  -76092.6358    0.9 -362947.0841 210761.8126  False
     0    212  -19342.6358    0.9 -222235.9802 183550.7087  False
     0    214  -58342.6358    0.9 -345197.0841 228511.8126  False
     0    216   45032.3642    0.9  -56583.9986 146648.7271  False
     0    218  -35342.6358    0.9 -322197.0841 251511.8126  False
     0    220  -49342.6358    0.9 -336197.0841 237511.8126  False
     0    221  -25342.6358    0.9 -312197.0841 261511.8126  False
     0    224   52407.3642    0.9   -91139.933 195954.6615  False
     0    226  -97342.6358    0.9 -300235.9802 105550.7087  False
     0    228  -21092.6358    0.9  -164639.933 122454.6615  False
     0    230  -28842.6358    0.9 -315697.0841 258011.8126  False
     0    234   60324.0309    0.9 -105383.9072 226031.9691  False
     0    236  -98701.1358    0.9 -301594.4802 104192.2087  False
     0    239  -47342.6358    0.9 -334197.0841 239511.8126  False
     0    240  -45092.6358    0.9 -247985.9802 157800.7087  False
     0    242  -55342.6358    0.9 -342197.0841 231511.8126  False
     0    244  -66917.6358    0.9  -210464.933  76629.6615  False
     0    248  -72342.6358    0.9 -359197.0841 214511.8126  False
     0    252  -29222.6358    0.9 -157651.0205   99205.749  False
     0    254  -42342.6358    0.9 -329197.0841 244511.8126  False
     0    259  -52842.6358    0.9 -339697.0841 234011.8126  False
     0    264  -25009.3024    0.9 -190717.2406 140698.6357  False
     0    268   32157.3642    0.9 -254697.0841 319011.8126  False
     0    272  -42342.6358    0.9 -329197.0841 244511.8126  False
     0    275  -24442.6358    0.9 -311297.0841 262411.8126  False
     0    280   47657.3642    0.9 -239197.0841 334511.8126  False
     0    286 -103342.6358    0.9 -390197.0841 183511.8126  False
     0    291  157657.3642    0.9 -129197.0841 444511.8126  False
     0    293  -58342.6358    0.9 -345197.0841 228511.8126  False
     0    294  -52342.6358    0.9 -339197.0841 234511.8126  False
     0    301    6657.3642    0.9 -280197.0841 293511.8126  False
     0    318   62057.3642    0.9 -103650.5739 227765.3024  False
     0    330  -38342.6358    0.9 -325197.0841 248511.8126  False
     0    386   83636.3642    0.9 -119256.9802 286529.7087  False
     0    552   52657.3642    0.9 -234197.0841 339511.8126  False
    19     20     -98000.0    0.9 -449225.4205 253225.4205  False
    19     24    -138000.0    0.9 -489225.4205 213225.4205  False
    19     30    -116000.0    0.9 -467225.4205 235225.4205  False
    19     32    -126825.0    0.9 -375178.8766 121528.8766  False
    19     34    -101325.0    0.9  -388099.355  185449.355  False
    19     36     -70550.0    0.9  -357324.355  216224.355  False
    19     37     120000.0    0.9 -231225.4205 471225.4205  False
    19     39     -39250.0    0.9  -326024.355  247524.355  False
    19     40  -58883.3333    0.9 -320671.3052 202904.6386  False
    19     42     -51000.0    0.9 -402225.4205 300225.4205  False
    19     44     -56500.0    0.9  -343274.355  230274.355  False
    19     48    -129000.0    0.9  -415774.355  157774.355  False
    19     50     -60000.0    0.9  -346774.355  226774.355  False
    19     52     -40800.0    0.9 -392025.4205 310425.4205  False
    19     54     -87000.0    0.9 -438225.4205 264225.4205  False
    19     56     -60566.0    0.9 -411791.4205 290659.4205  False
    19     60    -113000.0    0.9 -464225.4205 238225.4205  False
    19     64     -80000.0    0.9 -431225.4205 271225.4205  False
    19     67     -82500.0    0.9 -433725.4205 268725.4205  False
    19     68     -62500.0    0.9 -413725.4205 288725.4205  False
    19     70     -40000.0    0.9  -326774.355  246774.355  False
    19     77 -138333.3333    0.9 -400121.3052 123454.6386  False
    19     80    -109850.0    0.9  -396624.355  176924.355  False
    19     81     -81000.0    0.9 -432225.4205 270225.4205  False
    19     84     -93500.0    0.9  -380274.355  193274.355  False
    19     87    -151500.0    0.9 -502725.4205 199725.4205  False
    19     90    -133000.0    0.9 -484225.4205 218225.4205  False
    19     91    -144000.0    0.9 -495225.4205 207225.4205  False
    19     94    -134000.0    0.9  -420774.355  152774.355  False
    19     96 -118333.3333    0.9 -352483.6137  115816.947  False
    19     98    -117224.0    0.9 -468449.4205 234001.4205  False
    19     99     -37100.0    0.9 -277032.6394 202832.6394  False
    19    100    -159000.0    0.9  -445774.355  127774.355  False
    19    102     -99100.0    0.9 -333250.2803 135050.2803  False
    19    105    -101000.0    0.9 -452225.4205 250225.4205  False
    19    108  -83333.3333    0.9 -345121.3052 178454.6386  False
    19    112  -98532.3913    0.9 -309945.2507 112880.4681  False
    19    114     -77000.0    0.9 -316932.6394 162932.6394  False
    19    115    -139050.0    0.9  -425824.355  147724.355  False
    19    116     -95375.0    0.9 -343728.8766 152978.8766  False
    19    120     -90100.0    0.9 -330032.6394 149832.6394  False
    19    123     -65000.0    0.9 -416225.4205 286225.4205  False
    19    126     -77500.0    0.9 -339287.9719 184287.9719  False
    19    128    -111125.0    0.9 -359478.8766 137228.8766  False
    19    129    -131000.0    0.9 -482225.4205 220225.4205  False
    19    130    -105000.0    0.9 -456225.4205 246225.4205  False
    19    134     -92000.0    0.9  -378774.355  194774.355  False
    19    136     -92000.0    0.9 -443225.4205 259225.4205  False
    19    137     -96000.0    0.9  -382774.355  190774.355  False
    19    138    -118000.0    0.9  -404774.355  168774.355  False
    19    140    -120000.0    0.9 -471225.4205 231225.4205  False
    19    143     -60500.0    0.9  -347274.355  226274.355  False
    19    144  -83571.4286    0.9 -313502.4393 146359.5822  False
    19    145     -30000.0    0.9 -381225.4205 321225.4205  False
    19    148    -112500.0    0.9 -463725.4205 238725.4205  False
    19    150     -56300.0    0.9 -296232.6394 183632.6394  False
    19    154     -60600.0    0.9 -290531.0108 169331.0108  False
    19    156     -75000.0    0.9 -304931.0108 154931.0108  False
    19    158  -75533.3333    0.9 -337321.3052 186254.6386  False
    19    160     -91000.0    0.9 -352787.9719 170787.9719  False
    19    162     115000.0    0.9 -146787.9719 376787.9719  False
    19    164    -112520.0    0.9 -352452.6394 127412.6394  False
    19    168  -91833.3333    0.9 -353621.3052 169954.6386  False
    19    169     -45000.0    0.9 -396225.4205 306225.4205  False
    19    170      -6500.0    0.9  -293274.355  280274.355  False
    19    172    -180000.0    0.9 -531225.4205 171225.4205  False
    19    174      67000.0    0.9  -219774.355  353774.355  False
    19    176  -97366.6667    0.9 -359154.6386 164421.3052  False
    19    177     -21500.0    0.9 -372725.4205 329725.4205  False
    19    180     -61250.0    0.9  -348024.355  225524.355  False
    19    183     -68000.0    0.9 -419225.4205 283225.4205  False
    19    184  -92666.6667    0.9 -354454.6386 169121.3052  False
    19    185     -71000.0    0.9 -319353.8766 177353.8766  False
    19    189     -67000.0    0.9 -418225.4205 284225.4205  False
    19    190    -110750.0    0.9  -397524.355  176024.355  False
    19    192     -42500.0    0.9 -272431.0108 187431.0108  False
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   248    254      30000.0    0.9 -375560.1821 435560.1821  False
   248    259      19500.0    0.9 -386060.1821 425060.1821  False
   248    264   47333.3333    0.9 -283805.1687 378471.8354  False
   248    268     104500.0    0.9 -301060.1821 510060.1821  False
   248    272      30000.0    0.9 -375560.1821 435560.1821  False
   248    275      47900.0    0.9 -357660.1821 453460.1821  False
   248    280     120000.0    0.9 -285560.1821 525560.1821  False
   248    286     -31000.0    0.9 -436560.1821 374560.1821  False
   248    291     230000.0    0.9 -175560.1821 635560.1821  False
   248    293      14000.0    0.9 -391560.1821 419560.1821  False
   248    294      20000.0    0.9 -385560.1821 425560.1821  False
   248    301      79000.0    0.9 -326560.1821 484560.1821  False
   248    318     134400.0    0.9 -196738.5021 465538.5021  False
   248    330      34000.0    0.9 -371560.1821 439560.1821  False
   248    386     155979.0    0.9 -195246.4205 507204.4205  False
   248    552     125000.0    0.9 -280560.1821 530560.1821  False
   252    254     -13120.0    0.9 -327265.5663 301025.5663  False
   252    259     -23620.0    0.9 -337765.5663 290525.5663  False
   252    264    4213.3333    0.9 -205217.0442 213643.7108  False
   252    268      61380.0    0.9 -252765.5663 375525.5663  False
   252    272     -13120.0    0.9 -327265.5663 301025.5663  False
   252    275       4780.0    0.9 -309365.5663 318925.5663  False
   252    280      76880.0    0.9 -237265.5663 391025.5663  False
   252    286     -74120.0    0.9 -388265.5663 240025.5663  False
   252    291     186880.0    0.9 -127265.5663 501025.5663  False
   252    293     -29120.0    0.9 -343265.5663 285025.5663  False
   252    294     -23120.0    0.9 -337265.5663 291025.5663  False
   252    301      35880.0    0.9 -278265.5663 350025.5663  False
   252    318      91280.0    0.9 -118150.3775 300710.3775  False
   252    330      -9120.0    0.9 -323265.5663 305025.5663  False
   252    386     112859.0    0.9 -127073.6394 352791.6394  False
   252    552      81880.0    0.9 -232265.5663 396025.5663  False
   254    259     -10500.0    0.9 -416060.1821 395060.1821  False
   254    264   17333.3333    0.9 -313805.1687 348471.8354  False
   254    268      74500.0    0.9 -331060.1821 480060.1821  False
   254    272          0.0    0.9 -405560.1821 405560.1821  False
   254    275      17900.0    0.9 -387660.1821 423460.1821  False
   254    280      90000.0    0.9 -315560.1821 495560.1821  False
   254    286     -61000.0    0.9 -466560.1821 344560.1821  False
   254    291     200000.0    0.9 -205560.1821 605560.1821  False
   254    293     -16000.0    0.9 -421560.1821 389560.1821  False
   254    294     -10000.0    0.9 -415560.1821 395560.1821  False
   254    301      49000.0    0.9 -356560.1821 454560.1821  False
   254    318     104400.0    0.9 -226738.5021 435538.5021  False
   254    330       4000.0    0.9 -401560.1821 409560.1821  False
   254    386     125979.0    0.9 -225246.4205 477204.4205  False
   254    552      95000.0    0.9 -310560.1821 500560.1821  False
   259    264   27833.3333    0.9 -303305.1687 358971.8354  False
   259    268      85000.0    0.9 -320560.1821 490560.1821  False
   259    272      10500.0    0.9 -395060.1821 416060.1821  False
   259    275      28400.0    0.9 -377160.1821 433960.1821  False
   259    280     100500.0    0.9 -305060.1821 506060.1821  False
   259    286     -50500.0    0.9 -456060.1821 355060.1821  False
   259    291     210500.0    0.9 -195060.1821 616060.1821  False
   259    293      -5500.0    0.9 -411060.1821 400060.1821  False
   259    294        500.0    0.9 -405060.1821 406060.1821  False
   259    301      59500.0    0.9 -346060.1821 465060.1821  False
   259    318     114900.0    0.9 -216238.5021 446038.5021  False
   259    330      14500.0    0.9 -391060.1821 420060.1821  False
   259    386     136479.0    0.9 -214746.4205 487704.4205  False
   259    552     105500.0    0.9 -300060.1821 511060.1821  False
   264    268   57166.6667    0.9 -273971.8354 388305.1687  False
   264    272  -17333.3333    0.9 -348471.8354 313805.1687  False
   264    275     566.6667    0.9 -330571.8354 331705.1687  False
   264    280   72666.6667    0.9 -258471.8354 403805.1687  False
   264    286  -78333.3333    0.9 -409471.8354 252805.1687  False
   264    291  182666.6667    0.9 -148471.8354 513805.1687  False
   264    293  -33333.3333    0.9 -364471.8354 297805.1687  False
   264    294  -27333.3333    0.9 -358471.8354 303805.1687  False
   264    301   31666.6667    0.9 -299471.8354 362805.1687  False
   264    318   87066.6667    0.9 -147083.6137  321216.947  False
   264    330  -13333.3333    0.9 -344471.8354 317805.1687  False
   264    386  108645.6667    0.9 -153142.3052 370433.6386  False
   264    552   77666.6667    0.9 -253471.8354 408805.1687  False
   268    272     -74500.0    0.9 -480060.1821 331060.1821  False
   268    275     -56600.0    0.9 -462160.1821 348960.1821  False
   268    280      15500.0    0.9 -390060.1821 421060.1821  False
   268    286    -135500.0    0.9 -541060.1821 270060.1821  False
   268    291     125500.0    0.9 -280060.1821 531060.1821  False
   268    293     -90500.0    0.9 -496060.1821 315060.1821  False
   268    294     -84500.0    0.9 -490060.1821 321060.1821  False
   268    301     -25500.0    0.9 -431060.1821 380060.1821  False
   268    318      29900.0    0.9 -301238.5021 361038.5021  False
   268    330     -70500.0    0.9 -476060.1821 335060.1821  False
   268    386      51479.0    0.9 -299746.4205 402704.4205  False
   268    552      20500.0    0.9 -385060.1821 426060.1821  False
   272    275      17900.0    0.9 -387660.1821 423460.1821  False
   272    280      90000.0    0.9 -315560.1821 495560.1821  False
   272    286     -61000.0    0.9 -466560.1821 344560.1821  False
   272    291     200000.0    0.9 -205560.1821 605560.1821  False
   272    293     -16000.0    0.9 -421560.1821 389560.1821  False
   272    294     -10000.0    0.9 -415560.1821 395560.1821  False
   272    301      49000.0    0.9 -356560.1821 454560.1821  False
   272    318     104400.0    0.9 -226738.5021 435538.5021  False
   272    330       4000.0    0.9 -401560.1821 409560.1821  False
   272    386     125979.0    0.9 -225246.4205 477204.4205  False
   272    552      95000.0    0.9 -310560.1821 500560.1821  False
   275    280      72100.0    0.9 -333460.1821 477660.1821  False
   275    286     -78900.0    0.9 -484460.1821 326660.1821  False
   275    291     182100.0    0.9 -223460.1821 587660.1821  False
   275    293     -33900.0    0.9 -439460.1821 371660.1821  False
   275    294     -27900.0    0.9 -433460.1821 377660.1821  False
   275    301      31100.0    0.9 -374460.1821 436660.1821  False
   275    318      86500.0    0.9 -244638.5021 417638.5021  False
   275    330     -13900.0    0.9 -419460.1821 391660.1821  False
   275    386     108079.0    0.9 -243146.4205 459304.4205  False
   275    552      77100.0    0.9 -328460.1821 482660.1821  False
   280    286    -151000.0    0.9 -556560.1821 254560.1821  False
   280    291     110000.0    0.9 -295560.1821 515560.1821  False
   280    293    -106000.0    0.9 -511560.1821 299560.1821  False
   280    294    -100000.0    0.9 -505560.1821 305560.1821  False
   280    301     -41000.0    0.9 -446560.1821 364560.1821  False
   280    318      14400.0    0.9 -316738.5021 345538.5021  False
   280    330     -86000.0    0.9 -491560.1821 319560.1821  False
   280    386      35979.0    0.9 -315246.4205 387204.4205  False
   280    552       5000.0    0.9 -400560.1821 410560.1821  False
   286    291     261000.0    0.9 -144560.1821 666560.1821  False
   286    293      45000.0    0.9 -360560.1821 450560.1821  False
   286    294      51000.0    0.9 -354560.1821 456560.1821  False
   286    301     110000.0    0.9 -295560.1821 515560.1821  False
   286    318     165400.0    0.9 -165738.5021 496538.5021  False
   286    330      65000.0    0.9 -340560.1821 470560.1821  False
   286    386     186979.0    0.9 -164246.4205 538204.4205  False
   286    552     156000.0    0.9 -249560.1821 561560.1821  False
   291    293    -216000.0    0.9 -621560.1821 189560.1821  False
   291    294    -210000.0    0.9 -615560.1821 195560.1821  False
   291    301    -151000.0    0.9 -556560.1821 254560.1821  False
   291    318     -95600.0    0.9 -426738.5021 235538.5021  False
   291    330    -196000.0    0.9 -601560.1821 209560.1821  False
   291    386     -74021.0    0.9 -425246.4205 277204.4205  False
   291    552    -105000.0    0.9 -510560.1821 300560.1821  False
   293    294       6000.0    0.9 -399560.1821 411560.1821  False
   293    301      65000.0    0.9 -340560.1821 470560.1821  False
   293    318     120400.0    0.9 -210738.5021 451538.5021  False
   293    330      20000.0    0.9 -385560.1821 425560.1821  False
   293    386     141979.0    0.9 -209246.4205 493204.4205  False
   293    552     111000.0    0.9 -294560.1821 516560.1821  False
   294    301      59000.0    0.9 -346560.1821 464560.1821  False
   294    318     114400.0    0.9 -216738.5021 445538.5021  False
   294    330      14000.0    0.9 -391560.1821 419560.1821  False
   294    386     135979.0    0.9 -215246.4205 487204.4205  False
   294    552     105000.0    0.9 -300560.1821 510560.1821  False
   301    318      55400.0    0.9 -275738.5021 386538.5021  False
   301    330     -45000.0    0.9 -450560.1821 360560.1821  False
   301    386      76979.0    0.9 -274246.4205 428204.4205  False
   301    552      46000.0    0.9 -359560.1821 451560.1821  False
   318    330    -100400.0    0.9 -431538.5021 230738.5021  False
   318    386      21579.0    0.9 -240208.9719 283366.9719  False
   318    552      -9400.0    0.9 -340538.5021 321738.5021  False
   330    386     121979.0    0.9 -229246.4205 473204.4205  False
   330    552      91000.0    0.9 -314560.1821 496560.1821  False
   386    552     -30979.0    0.9 -382204.4205 320246.4205  False
-----------------------------------------------------------------


sns.scatterplot(x = data['EnclosedPorch'], y = data.Property_Sale_Price, data = data )
plt.xlabel('EnclosedPorch')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of EnclosedPorch vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter Plot Of EnclosedPorch vs Property_Sale_Price')


data['ThrSsnPorch'].isnull().sum()

0


data['ThrSsnPorch'].describe()

count    2073.000000
mean        2.979257
std        26.817218
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max       508.000000
Name: ThrSsnPorch, dtype: float64


data['ThrSsnPorch'].value_counts()

0      2041
168       6
23        3
304       2
182       2
180       2
216       2
162       2
144       2
196       1
96        1
238       1
290       1
140       1
320       1
508       1
130       1
153       1
245       1
407       1
Name: ThrSsnPorch, dtype: int64


data['ThrSsnPorch'].value_counts().sum()

2073


sns.displot(data['ThrSsnPorch'])
plt.title('Histogram Of ThrSsnPorch')

Text(0.5, 1.0, 'Histogram Of ThrSsnPorch')


sns.distplot(data['ThrSsnPorch'])
plt.title('Normality Of ThrSsnPorch')

Text(0.5, 1.0, 'Normality Of ThrSsnPorch')


sns.countplot(data['ThrSsnPorch'])
plt.title('ThrSsnPorch - count')

Text(0.5, 1.0, 'ThrSsnPorch - count')


sns.boxplot(data['ThrSsnPorch'], color = 'red')
plt.title('BoxPlot Of ThrSsnPorch')

Text(0.5, 1.0, 'BoxPlot Of ThrSsnPorch')


IQR = data['ThrSsnPorch'].quantile(0.75) - data['ThrSsnPorch'].quantile(0.25)
IQR

0.0


UL = data['ThrSsnPorch'].quantile(0.75) + (1.5*IQR)
UL

0.0


LL = data['ThrSsnPorch'].quantile(0.25) - (1.5*IQR)
LL

0.0


np.corrcoef(data['ThrSsnPorch'], data.Property_Sale_Price)

array([[1.        , 0.05601521],
       [0.05601521, 1.        ]])


tukey_EP = pairwise_tukeyhsd(data.Property_Sale_Price, data['ThrSsnPorch'], alpha = 0.05)
print(tukey_EP)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
     0     23   -7166.5546    0.9 -144350.1147 130017.0054  False
     0     96    2743.4454    0.9 -234749.1751 240236.0659  False
     0    130    2843.4454    0.9 -234649.1751 240336.0659  False
     0    140  -42156.5546    0.9 -279649.1751 195336.0659  False
     0    144   34343.4454    0.9 -133630.3117 202317.2024  False
     0    153  162843.4454 0.6147  -74649.1751 400336.0659  False
     0    162  -27656.5546    0.9 -195630.3117 140317.2024  False
     0    168   43176.7787    0.9  -53897.8072 140251.3646  False
     0    180  -42156.5546    0.9 -210130.3117 125817.2024  False
     0    182   44843.4454    0.9 -123130.3117 212817.2024  False
     0    196   51343.4454    0.9 -186149.1751 288836.0659  False
     0    216    7343.4454    0.9 -160630.3117 175317.2024  False
     0    238   17343.4454    0.9 -220149.1751 254836.0659  False
     0    245   54343.4454    0.9 -183149.1751 291836.0659  False
     0    290   85343.4454    0.9 -152149.1751 322836.0659  False
     0    304  162843.4454  0.071   -5130.3117 330817.2024  False
     0    320  -34156.5546    0.9 -271649.1751 203336.0659  False
     0    407    3343.4454    0.9 -234149.1751 240836.0659  False
     0    508    3343.4454    0.9 -234149.1751 240836.0659  False
    23     96       9910.0    0.9 -264255.7004 284075.7004  False
    23    130      10010.0    0.9 -264155.7004 284175.7004  False
    23    140     -34990.0    0.9 -309155.7004 239175.7004  False
    23    144      41510.0    0.9 -175237.0174 258257.0174  False
    23    153     170010.0  0.768 -104155.7004 444175.7004  False
    23    162     -20490.0    0.9 -237237.0174 196257.0174  False
    23    168   50343.3333    0.9 -117548.1844 218234.8511  False
    23    180     -34990.0    0.9 -251737.0174 181757.0174  False
    23    182      52010.0    0.9 -164737.0174 268757.0174  False
    23    196      58510.0    0.9 -215655.7004 332675.7004  False
    23    216      14510.0    0.9 -202237.0174 231257.0174  False
    23    238      24510.0    0.9 -249655.7004 298675.7004  False
    23    245      61510.0    0.9 -212655.7004 335675.7004  False
    23    290      92510.0    0.9 -181655.7004 366675.7004  False
    23    304     170010.0 0.3709  -46737.0174 386757.0174  False
    23    320     -26990.0    0.9 -301155.7004 247175.7004  False
    23    407      10510.0    0.9 -263655.7004 284675.7004  False
    23    508      10510.0    0.9 -263655.7004 284675.7004  False
    96    130        100.0    0.9 -335683.0355 335883.0355  False
    96    140     -44900.0    0.9 -380683.0355 290883.0355  False
    96    144      31600.0    0.9 -259196.6389 322396.6389  False
    96    153     160100.0    0.9 -175683.0355 495883.0355  False
    96    162     -30400.0    0.9 -321196.6389 260396.6389  False
    96    168   40433.3333    0.9 -216025.1962 296891.8629  False
    96    180     -44900.0    0.9 -335696.6389 245896.6389  False
    96    182      42100.0    0.9 -248696.6389 332896.6389  False
    96    196      48600.0    0.9 -287183.0355 384383.0355  False
    96    216       4600.0    0.9 -286196.6389 295396.6389  False
    96    238      14600.0    0.9 -321183.0355 350383.0355  False
    96    245      51600.0    0.9 -284183.0355 387383.0355  False
    96    290      82600.0    0.9 -253183.0355 418383.0355  False
    96    304     160100.0    0.9 -130696.6389 450896.6389  False
    96    320     -36900.0    0.9 -372683.0355 298883.0355  False
    96    407        600.0    0.9 -335183.0355 336383.0355  False
    96    508        600.0    0.9 -335183.0355 336383.0355  False
   130    140     -45000.0    0.9 -380783.0355 290783.0355  False
   130    144      31500.0    0.9 -259296.6389 322296.6389  False
   130    153     160000.0    0.9 -175783.0355 495783.0355  False
   130    162     -30500.0    0.9 -321296.6389 260296.6389  False
   130    168   40333.3333    0.9 -216125.1962 296791.8629  False
   130    180     -45000.0    0.9 -335796.6389 245796.6389  False
   130    182      42000.0    0.9 -248796.6389 332796.6389  False
   130    196      48500.0    0.9 -287283.0355 384283.0355  False
   130    216       4500.0    0.9 -286296.6389 295296.6389  False
   130    238      14500.0    0.9 -321283.0355 350283.0355  False
   130    245      51500.0    0.9 -284283.0355 387283.0355  False
   130    290      82500.0    0.9 -253283.0355 418283.0355  False
   130    304     160000.0    0.9 -130796.6389 450796.6389  False
   130    320     -37000.0    0.9 -372783.0355 298783.0355  False
   130    407        500.0    0.9 -335283.0355 336283.0355  False
   130    508        500.0    0.9 -335283.0355 336283.0355  False
   140    144      76500.0    0.9 -214296.6389 367296.6389  False
   140    153     205000.0 0.7904 -130783.0355 540783.0355  False
   140    162      14500.0    0.9 -276296.6389 305296.6389  False
   140    168   85333.3333    0.9 -171125.1962 341791.8629  False
   140    180          0.0    0.9 -290796.6389 290796.6389  False
   140    182      87000.0    0.9 -203796.6389 377796.6389  False
   140    196      93500.0    0.9 -242283.0355 429283.0355  False
   140    216      49500.0    0.9 -241296.6389 340296.6389  False
   140    238      59500.0    0.9 -276283.0355 395283.0355  False
   140    245      96500.0    0.9 -239283.0355 432283.0355  False
   140    290     127500.0    0.9 -208283.0355 463283.0355  False
   140    304     205000.0 0.5697  -85796.6389 495796.6389  False
   140    320       8000.0    0.9 -327783.0355 343783.0355  False
   140    407      45500.0    0.9 -290283.0355 381283.0355  False
   140    508      45500.0    0.9 -290283.0355 381283.0355  False
   144    153     128500.0    0.9 -162296.6389 419296.6389  False
   144    162     -62000.0    0.9 -299434.4614 175434.4614  False
   144    168    8833.3333    0.9 -185031.0926 202697.7593  False
   144    180     -76500.0    0.9 -313934.4614 160934.4614  False
   144    182      10500.0    0.9 -226934.4614 247934.4614  False
   144    196      17000.0    0.9 -273796.6389 307796.6389  False
   144    216     -27000.0    0.9 -264434.4614 210434.4614  False
   144    238     -17000.0    0.9 -307796.6389 273796.6389  False
   144    245      20000.0    0.9 -270796.6389 310796.6389  False
   144    290      51000.0    0.9 -239796.6389 341796.6389  False
   144    304     128500.0    0.9 -108934.4614 365934.4614  False
   144    320     -68500.0    0.9 -359296.6389 222296.6389  False
   144    407     -31000.0    0.9 -321796.6389 259796.6389  False
   144    508     -31000.0    0.9 -321796.6389 259796.6389  False
   153    162    -190500.0 0.6862 -481296.6389 100296.6389  False
   153    168 -119666.6667    0.9 -376125.1962 136791.8629  False
   153    180    -205000.0 0.5697 -495796.6389  85796.6389  False
   153    182    -118000.0    0.9 -408796.6389 172796.6389  False
   153    196    -111500.0    0.9 -447283.0355 224283.0355  False
   153    216    -155500.0    0.9 -446296.6389 135296.6389  False
   153    238    -145500.0    0.9 -481283.0355 190283.0355  False
   153    245    -108500.0    0.9 -444283.0355 227283.0355  False
   153    290     -77500.0    0.9 -413283.0355 258283.0355  False
   153    304          0.0    0.9 -290796.6389 290796.6389  False
   153    320    -197000.0  0.846 -532783.0355 138783.0355  False
   153    407    -159500.0    0.9 -495283.0355 176283.0355  False
   153    508    -159500.0    0.9 -495283.0355 176283.0355  False
   162    168   70833.3333    0.9 -123031.0926 264697.7593  False
   162    180     -14500.0    0.9 -251934.4614 222934.4614  False
   162    182      72500.0    0.9 -164934.4614 309934.4614  False
   162    196      79000.0    0.9 -211796.6389 369796.6389  False
   162    216      35000.0    0.9 -202434.4614 272434.4614  False
   162    238      45000.0    0.9 -245796.6389 335796.6389  False
   162    245      82000.0    0.9 -208796.6389 372796.6389  False
   162    290     113000.0    0.9 -177796.6389 403796.6389  False
   162    304     190500.0 0.3246  -46934.4614 427934.4614  False
   162    320      -6500.0    0.9 -297296.6389 284296.6389  False
   162    407      31000.0    0.9 -259796.6389 321796.6389  False
   162    508      31000.0    0.9 -259796.6389 321796.6389  False
   168    180  -85333.3333    0.9 -279197.7593 108531.0926  False
   168    182    1666.6667    0.9 -192197.7593 195531.0926  False
   168    196    8166.6667    0.9 -248291.8629 264625.1962  False
   168    216  -35833.3333    0.9 -229697.7593 158031.0926  False
   168    238  -25833.3333    0.9 -282291.8629 230625.1962  False
   168    245   11166.6667    0.9 -245291.8629 267625.1962  False
   168    290   42166.6667    0.9 -214291.8629 298625.1962  False
   168    304  119666.6667 0.7746  -74197.7593 313531.0926  False
   168    320  -77333.3333    0.9 -333791.8629 179125.1962  False
   168    407  -39833.3333    0.9 -296291.8629 216625.1962  False
   168    508  -39833.3333    0.9 -296291.8629 216625.1962  False
   180    182      87000.0    0.9 -150434.4614 324434.4614  False
   180    196      93500.0    0.9 -197296.6389 384296.6389  False
   180    216      49500.0    0.9 -187934.4614 286934.4614  False
   180    238      59500.0    0.9 -231296.6389 350296.6389  False
   180    245      96500.0    0.9 -194296.6389 387296.6389  False
   180    290     127500.0    0.9 -163296.6389 418296.6389  False
   180    304     205000.0 0.1997  -32434.4614 442434.4614  False
   180    320       8000.0    0.9 -282796.6389 298796.6389  False
   180    407      45500.0    0.9 -245296.6389 336296.6389  False
   180    508      45500.0    0.9 -245296.6389 336296.6389  False
   182    196       6500.0    0.9 -284296.6389 297296.6389  False
   182    216     -37500.0    0.9 -274934.4614 199934.4614  False
   182    238     -27500.0    0.9 -318296.6389 263296.6389  False
   182    245       9500.0    0.9 -281296.6389 300296.6389  False
   182    290      40500.0    0.9 -250296.6389 331296.6389  False
   182    304     118000.0    0.9 -119434.4614 355434.4614  False
   182    320     -79000.0    0.9 -369796.6389 211796.6389  False
   182    407     -41500.0    0.9 -332296.6389 249296.6389  False
   182    508     -41500.0    0.9 -332296.6389 249296.6389  False
   196    216     -44000.0    0.9 -334796.6389 246796.6389  False
   196    238     -34000.0    0.9 -369783.0355 301783.0355  False
   196    245       3000.0    0.9 -332783.0355 338783.0355  False
   196    290      34000.0    0.9 -301783.0355 369783.0355  False
   196    304     111500.0    0.9 -179296.6389 402296.6389  False
   196    320     -85500.0    0.9 -421283.0355 250283.0355  False
   196    407     -48000.0    0.9 -383783.0355 287783.0355  False
   196    508     -48000.0    0.9 -383783.0355 287783.0355  False
   216    238      10000.0    0.9 -280796.6389 300796.6389  False
   216    245      47000.0    0.9 -243796.6389 337796.6389  False
   216    290      78000.0    0.9 -212796.6389 368796.6389  False
   216    304     155500.0 0.6866  -81934.4614 392934.4614  False
   216    320     -41500.0    0.9 -332296.6389 249296.6389  False
   216    407      -4000.0    0.9 -294796.6389 286796.6389  False
   216    508      -4000.0    0.9 -294796.6389 286796.6389  False
   238    245      37000.0    0.9 -298783.0355 372783.0355  False
   238    290      68000.0    0.9 -267783.0355 403783.0355  False
   238    304     145500.0    0.9 -145296.6389 436296.6389  False
   238    320     -51500.0    0.9 -387283.0355 284283.0355  False
   238    407     -14000.0    0.9 -349783.0355 321783.0355  False
   238    508     -14000.0    0.9 -349783.0355 321783.0355  False
   245    290      31000.0    0.9 -304783.0355 366783.0355  False
   245    304     108500.0    0.9 -182296.6389 399296.6389  False
   245    320     -88500.0    0.9 -424283.0355 247283.0355  False
   245    407     -51000.0    0.9 -386783.0355 284783.0355  False
   245    508     -51000.0    0.9 -386783.0355 284783.0355  False
   290    304      77500.0    0.9 -213296.6389 368296.6389  False
   290    320    -119500.0    0.9 -455283.0355 216283.0355  False
   290    407     -82000.0    0.9 -417783.0355 253783.0355  False
   290    508     -82000.0    0.9 -417783.0355 253783.0355  False
   304    320    -197000.0 0.6339 -487796.6389  93796.6389  False
   304    407    -159500.0    0.9 -450296.6389 131296.6389  False
   304    508    -159500.0    0.9 -450296.6389 131296.6389  False
   320    407      37500.0    0.9 -298283.0355 373283.0355  False
   320    508      37500.0    0.9 -298283.0355 373283.0355  False
   407    508          0.0    0.9 -335783.0355 335783.0355  False
-----------------------------------------------------------------


sns.scatterplot(x = data['ThrSsnPorch'], y = data.Property_Sale_Price, data = data )
plt.xlabel('ThrSsnPorch')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of ThrSsnPorch vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter Plot Of ThrSsnPorch vs Property_Sale_Price')


data.ScreenPorch.isnull().sum()

0


data.ScreenPorch.describe()

count    2073.000000
mean       14.552340
std        54.099626
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max       480.000000
Name: ScreenPorch, dtype: float64


data.ScreenPorch.value_counts()

0      1912
168       8
120       7
192       7
224       6
       ... 
374       1
396       1
410       1
440       1
385       1
Name: ScreenPorch, Length: 76, dtype: int64


data.ScreenPorch.value_counts().sum()

2073


sns.displot(data.ScreenPorch)
plt.title('Histogram Of ScreenPorch')

Text(0.5, 1.0, 'Histogram Of ScreenPorch')


sns.distplot(data.ScreenPorch)
plt.title('Normality Of ScreenPorch')

Text(0.5, 1.0, 'Normality Of ScreenPorch')


sns.countplot(data.ScreenPorch)
plt.title('ScreenPorch - count')

Text(0.5, 1.0, 'ScreenPorch - count')


sns.boxplot(data.ScreenPorch, color = 'red')
plt.title('BoxPlot Of ScreenPorch')

Text(0.5, 1.0, 'BoxPlot Of ScreenPorch')


IQR = data.ScreenPorch.quantile(0.75) - data.ScreenPorch.quantile(0.25)
IQR

0.0


UL = data.ScreenPorch.quantile(0.75) + (1.5*IQR)
UL

0.0


LL = data.ScreenPorch.quantile(0.25) - (1.5*IQR)
LL

0.0


np.corrcoef(data.ScreenPorch, data.Property_Sale_Price)

array([[1.        , 0.10780137],
       [0.10780137, 1.        ]])


sns.scatterplot(x = data['ScreenPorch'], y = data.Property_Sale_Price, data = data )
plt.xlabel('ScreenPorch')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of ScreenPorch vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter Plot Of ScreenPorch vs Property_Sale_Price')


data.PoolArea.isnull().sum()

0


data.PoolArea.describe()

count    2073.000000
mean        2.299083
std        37.411031
min         0.000000
25%         0.000000
50%         0.000000
75%         0.000000
max       738.000000
Name: PoolArea, dtype: float64


data.PoolArea.value_counts()

0      2065
738       2
480       1
512       1
576       1
648       1
519       1
555       1
Name: PoolArea, dtype: int64


data.PoolArea.value_counts().sum()

2073


sns.displot(data.PoolArea)
plt.title('Histogram Of PoolArea')

Text(0.5, 1.0, 'Histogram Of PoolArea')


sns.distplot(data.PoolArea)
plt.title('Normality Of PoolArea')

Text(0.5, 1.0, 'Normality Of PoolArea')


sns.countplot(data.PoolArea)
plt.title('PoolArea - count')

Text(0.5, 1.0, 'PoolArea - count')


sns.boxplot(data.PoolArea, color = 'red')
plt.title('BoxPlot Of PoolArea')

Text(0.5, 1.0, 'BoxPlot Of PoolArea')


IQR = data.PoolArea.quantile(0.75) - data.PoolArea.quantile(0.25)
IQR

0.0


UL = data.PoolArea.quantile(0.75) + (1.5*IQR)
UL

0.0


LL = data.PoolArea.quantile(0.25) - (1.5*IQR)
LL

0.0


np.corrcoef(data.PoolArea, data.Property_Sale_Price)

array([[1.        , 0.05597028],
       [0.05597028, 1.        ]])


sns.scatterplot(x = data['PoolArea'], y = data.Property_Sale_Price, data = data )
plt.xlabel('PoolArea')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of PoolArea vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter Plot Of PoolArea vs Property_Sale_Price')


data.PoolQC.isnull().sum()

2065


data.PoolQC.dtype

dtype('O')


data['PoolQC'] = data['PoolQC'].fillna('No Pool')


data.PoolQC.isnull().sum()

0


data.PoolQC.value_counts()

No Pool    2065
Gd            4
Fa            2
Ex            2
Name: PoolQC, dtype: int64


data.PoolQC.value_counts().sum()

2073


sns.countplot(data.PoolQC)
plt.title('PoolQC - count')

Text(0.5, 1.0, 'PoolQC - count')


data.groupby("PoolQC")["Property_Sale_Price"].describe()


mod_PQC = ols('Property_Sale_Price ~ PoolQC', data = data).fit()
sm.stats.anova_lm(mod_PQC)


tukey_PQC = pairwise_tukeyhsd(data.Property_Sale_Price, data.PoolQC, alpha = 0.05)
print(tukey_PQC)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05        
==================================================================
group1  group2   meandiff   p-adj     lower        upper    reject
------------------------------------------------------------------
    Ex      Fa     -72000.0 0.6816 -244141.7394 100141.7394  False
    Ex      Gd     -67265.0 0.6341 -216344.1193  81814.1193  False
    Ex No Pool -110133.1923 0.0926 -231914.7148  11648.3303  False
    Fa      Gd       4735.0    0.9 -144344.1193 153814.1193  False
    Fa No Pool  -38133.1923 0.8333 -159914.7148  83648.3303  False
    Gd No Pool  -42868.1923 0.5672 -129022.3832  43285.9987  False
------------------------------------------------------------------


data.Fence.isnull().sum()

1669


data.Fence.dtype

dtype('O')


data['Fence'] = data['Fence'].fillna('No Fence')


data.Fence.isnull().sum()

0


data.Fence.value_counts()

No Fence    1669
MnPrv        233
GdPrv         88
GdWo          69
MnWw          14
Name: Fence, dtype: int64


data.Fence.value_counts().sum()

2073


sns.countplot(data.Fence)
plt.title('Fence - count')

Text(0.5, 1.0, 'Fence - count')


data.groupby("Fence")["Property_Sale_Price"].describe()


mod_F = ols('Property_Sale_Price ~ Fence', data = data).fit()
sm.stats.anova_lm(mod_F)


tukey_F = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fence, alpha = 0.05)
print(tukey_F)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1  group2    meandiff  p-adj     lower       upper    reject
-----------------------------------------------------------------
 GdPrv     GdWo -43354.3159  0.001 -72060.5163 -14648.1154   True
 GdPrv    MnPrv -32614.5348  0.001 -54951.5422 -10277.5274   True
 GdPrv     MnWw -41218.7175 0.1834 -92585.9894  10148.5543  False
 GdPrv No Fence   6272.8474    0.9 -13252.9215  25798.6163  False
  GdWo    MnPrv  10739.7811 0.7253 -13727.8957  35207.4578  False
  GdWo     MnWw   2135.5983    0.9 -50193.3795  54464.5762  False
  GdWo No Fence  49627.1633  0.001  27695.8852  71558.4413   True
 MnPrv     MnWw  -8604.1827    0.9 -57728.6967  40520.3313  False
 MnPrv No Fence  38887.3822  0.001  26402.3147  51372.4498   True
  MnWw No Fence  47491.5649 0.0534   -420.1417  95403.2715  False
-----------------------------------------------------------------


data["Fence"].replace('GdPrv', 'No Fence',inplace = True)
data["Fence"].replace('MnWw', 'GdWo',inplace = True)


data.Fence.value_counts()

No Fence    1757
MnPrv        233
GdWo          83
Name: Fence, dtype: int64


data.Fence.value_counts().sum()

2073


mod_F = ols('Property_Sale_Price ~ Fence', data = data).fit()
sm.stats.anova_lm(mod_F)


tukey_F = pairwise_tukeyhsd(data.Property_Sale_Price, data.Fence, alpha = 0.05)
print(tukey_F)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1  group2   meandiff  p-adj    lower      upper    reject
--------------------------------------------------------------
  GdWo    MnPrv 10379.5596 0.4303 -9218.8274 29977.9467  False
  GdWo No Fence  48952.764  0.001 31730.9942 66174.5338   True
 MnPrv No Fence 38573.2043  0.001 27883.7264 49262.6822   True
--------------------------------------------------------------


data["Fence"].replace('GdWo', 'MnPrv',inplace = True)


data.Fence.value_counts()

No Fence    1757
MnPrv        316
Name: Fence, dtype: int64


data.Fence.value_counts().sum()

2073


F_NF = data[data.Fence == 'No Fence']
F_M = data[data.Fence == 'MnPrv']
ttest_ind(F_NF.Property_Sale_Price, F_M.Property_Sale_Price)

Ttest_indResult(statistic=10.338164977422487, pvalue=1.8417334925793386e-24)


sns.countplot(x = 'Fence', data = data)
plt.title('Fence - count')

Text(0.5, 1.0, 'Fence - count')


sns.catplot(x = 'Fence', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of Fence vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of Fence vs Property_Sale_Price')


sns.boxplot(x = 'Fence', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of Fence vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot Of Fence vs Property_Sale_Price')


data.MiscFeature.isnull().sum()

1993


data.MiscFeature.dtype

dtype('O')


data['MiscFeature'] = data['MiscFeature'].fillna('Nothing')


data.MiscFeature.isnull().sum()

0


data.MiscFeature.value_counts()

Nothing    1993
Shed         74
Othr          3
Gar2          2
TenC          1
Name: MiscFeature, dtype: int64


data.MiscFeature.value_counts().sum()

2073


sns.countplot(data.MiscFeature)
plt.title('MiscFeature - count')

Text(0.5, 1.0, 'MiscFeature - count')


data.groupby("MiscFeature")["Property_Sale_Price"].describe()


mod_MF = ols('Property_Sale_Price ~ MiscFeature', data = data).fit()
sm.stats.anova_lm(mod_MF)


tukey_MF = pairwise_tukeyhsd(data.Property_Sale_Price, data.MiscFeature, alpha = 0.05)
print(tukey_MF)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05        
==================================================================
 group1  group2   meandiff  p-adj     lower        upper    reject
------------------------------------------------------------------
   Gar2 Nothing   7969.7466    0.9 -121046.0559 136985.5491  False
   Gar2    Othr    -63750.0  0.812 -230225.1758 102725.1758  False
   Gar2    Shed -21446.7162    0.9 -152128.7978 109235.3654  False
   Gar2    TenC     79250.0 0.8555 -144099.8858 302599.8858  False
Nothing    Othr -71719.7466 0.3406 -177087.1062  33647.6129  False
Nothing    Shed -29416.4628 0.0019  -51005.8758  -7827.0498   True
Nothing    TenC  71280.2534 0.7999 -111129.9102  253690.417  False
   Othr    Shed  42303.2838  0.795  -65097.8783 149704.4459  False
   Othr    TenC    143000.0 0.3431  -67576.2918 353576.2918  False
   Shed    TenC 100696.7162 0.5556   -82895.759 284289.1914  False
------------------------------------------------------------------


data["MiscFeature"].replace('Gar2', 'Nothing', inplace = True)
data["MiscFeature"].replace('Othr', 'Nothing', inplace = True)
data["MiscFeature"].replace('TenC', 'Nothing', inplace = True)


data.MiscFeature.value_counts()

Nothing    1999
Shed         74
Name: MiscFeature, dtype: int64


data.MiscFeature.value_counts().sum()

2073


MF_N = data[data.MiscFeature == 'Nothing']
MF_S = data[data.MiscFeature == 'Shed']
ttest_ind(MF_N.Property_Sale_Price, MF_S.Property_Sale_Price)

Ttest_indResult(statistic=3.7086072529736223, pvalue=0.00021388527972514962)


sns.countplot(x = 'MiscFeature', data = data)
plt.title('MiscFeature - count')

Text(0.5, 1.0, 'MiscFeature - count')


sns.catplot(x = 'MiscFeature', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of MiscFeature vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of MiscFeature vs Property_Sale_Price')


data.MiscVal.isnull().sum()

0


data.MiscVal.describe()

count     2073.000000
mean        39.736614
std        429.819512
min          0.000000
25%          0.000000
50%          0.000000
75%          0.000000
max      15500.000000
Name: MiscVal, dtype: float64


data.MiscVal.value_counts()

0        1996
400        21
500        11
450         6
700         6
2000        5
600         4
2500        3
1200        3
620         3
480         3
560         2
1300        2
54          1
800         1
1150        1
1400        1
350         1
8300        1
15500       1
3500        1
Name: MiscVal, dtype: int64


data.MiscVal.value_counts().sum()

2073


sns.displot(data.MiscVal)
plt.title('Histogram Of MiscVal')

Text(0.5, 1.0, 'Histogram Of MiscVal')


sns.distplot(data.MiscVal)
plt.title('Normality Of MiscVal')

Text(0.5, 1.0, 'Normality Of MiscVal')


sns.countplot(data.MiscVal)
plt.title('MiscVal - count')

Text(0.5, 1.0, 'MiscVal - count')


sns.boxplot(data.MiscVal, color = 'red')
plt.title('BoxPlot Of MiscVal')

Text(0.5, 1.0, 'BoxPlot Of MiscVal')


IQR = data.MiscVal.quantile(0.75) - data.MiscVal.quantile(0.25)
IQR

0.0


UL = data.MiscVal.quantile(0.75) + (1.5*IQR)
UL

0.0


LL = data.MiscVal.quantile(0.25) - (1.5*IQR)
LL

0.0


np.corrcoef(data.MiscVal, data.Property_Sale_Price)

array([[ 1.        , -0.01932011],
       [-0.01932011,  1.        ]])


mod_cu = ols('Property_Sale_Price ~ MiscVal', data = data).fit()
sm.stats.anova_lm(mod_cu)


sns.scatterplot(x = data['MiscVal'], y = data.Property_Sale_Price, data = data )
plt.xlabel('MiscVal')
plt.ylabel('Property_Sale_Price')
plt.title('Scatter Plot Of MiscVal vs Property_Sale_Price')

Text(0.5, 1.0, 'Scatter Plot Of MiscVal vs Property_Sale_Price')


data.MoSold.isnull().sum()

0


data.MoSold.describe()

count    2073.000000
mean        6.287506
std         2.690130
min         1.000000
25%         5.000000
50%         6.000000
75%         8.000000
max        12.000000
Name: MoSold, dtype: float64


data.MoSold.value_counts()

6     356
7     325
5     294
4     201
8     187
3     140
10    130
11    111
9      87
1      85
2      84
12     73
Name: MoSold, dtype: int64


data.MoSold.value_counts().sum()

2073


sns.countplot(data.MoSold)
plt.title('MoSold - count')

Text(0.5, 1.0, 'MoSold - count')


data.groupby("MoSold")["Property_Sale_Price"].describe()


mod_MS = ols('Property_Sale_Price ~ MoSold', data = data).fit()
sm.stats.anova_lm(mod_MS)


tukey_MS = pairwise_tukeyhsd(data.Property_Sale_Price, data.MoSold, alpha = 0.05)
print(tukey_MS)

     Multiple Comparison of Means - Tukey HSD, FWER=0.05      
==============================================================
group1 group2   meandiff  p-adj     lower      upper    reject
--------------------------------------------------------------
     1      2  11275.0097    0.9 -22368.2411 44918.2604  False
     1      3   7004.6811    0.9  -23064.492 37073.8542  False
     1      4  -2557.9063    0.9 -30850.9214 25735.1088  False
     1      5   3049.8413    0.9 -23880.3962 29980.0788  False
     1      6   3425.7725    0.9 -22973.2921 29824.8371  False
     1      7    9382.819    0.9  -17257.811  36023.449  False
     1      8   9903.1775    0.9 -18702.8724 38509.2275  False
     1      9  15581.0158    0.9 -17769.2147 48931.2464  False
     1     10   1672.0344    0.9 -28830.9124 32174.9812  False
     1     11  28680.0621 0.1164  -2838.0931 60198.2173  False
     1     12  14004.9143    0.9 -20889.9374  48899.766  False
     2      3  -4270.3286    0.9 -34450.6635 25910.0063  False
     2      4  -13832.916    0.9 -42244.0432 14578.2112  False
     2      5  -8225.1684    0.9 -35279.4681 18829.1314  False
     2      6  -7849.2372    0.9 -34374.8485 18676.3742  False
     2      7  -1892.1907    0.9 -28658.2254  24873.844  False
     2      8  -1371.8321    0.9 -30094.7069 27351.0427  False
     2      9   4306.0062    0.9 -29144.4845 37756.4968  False
     2     10  -9602.9753    0.9 -40215.5088 21009.5583  False
     2     11  17405.0524 0.7931 -14219.1717 49029.2766  False
     2     12   2729.9046    0.9 -32260.7816 37720.5908  False
     3      4  -9562.5874    0.9 -33634.9573 14509.7826  False
     3      5  -3954.8398    0.9 -26409.7275 18500.0479  False
     3      6  -3578.9086    0.9 -25393.9242  18236.107  False
     3      7   2378.1379    0.9 -19728.5909 24484.8668  False
     3      8   2898.4964    0.9 -21541.0286 27338.0215  False
     3      9   8576.3347    0.9 -21277.0096 38429.6791  False
     3     10  -5332.6467    0.9 -31967.4746 21302.1812  False
     3     11   21675.381 0.3087  -6116.3156 49467.0776  False
     3     12   7000.2332    0.9 -24569.2927 38569.7591  False
     4      5   5607.7476    0.9 -14406.2854 25621.7806  False
     4      6   5983.6788    0.9 -13309.6993 25277.0569  False
     4      7  11940.7253 0.6738  -7681.8891 31563.3397  False
     4      8  12461.0838 0.7716     -9756.7 34678.8677  False
     4      9  18138.9221 0.5961  -9924.6077 46202.4519  False
     4     10   4229.9407    0.9 -20382.1212 28842.0026  False
     4     11  31237.9684 0.0046   5378.3873 57097.5495   True
     4     12  16562.8206 0.7851 -13319.8584 46445.4995  False
     5      6    375.9312    0.9 -16857.1018 17608.9642  False
     5      7   6332.9777    0.9  -11267.874 23933.8295  False
     5      8   6853.3362    0.9 -13600.8297 27307.5022  False
     5      9  12531.1745    0.9 -14157.8624 39220.2115  False
     5     10  -1377.8069    0.9 -24410.3182 21654.7044  False
     5     11  25630.2208 0.0291   1269.1674 49991.2742   True
     5     12   10955.073    0.9  -17640.687 39550.8329  False
     6      7   5957.0465    0.9 -10819.8097 22733.9027  False
     6      8    6477.405    0.9 -13272.1729  26226.983  False
     6      9  12155.2433    0.9 -13997.7224  38308.209  False
     6     10  -1753.7381    0.9 -24162.8769 20655.4006  False
     6     11  25254.2896  0.026   1481.7457 49026.8335   True
     6     12  10579.1418    0.9 -17516.9507 38675.2342  False
     7      8    520.3585    0.9 -19550.9738 20591.6909  False
     7      9   6198.1968    0.9 -20198.5865 32594.9801  False
     7     10  -7710.7846    0.9 -30404.0007 14982.4314  False
     7     11  19297.2431 0.2656  -4743.2727 43337.7589  False
     7     12   4622.0953    0.9 -23701.0925  32945.283  False
     8      9   5677.8383    0.9 -22701.2578 34056.9343  False
     8     10  -8231.1432    0.9 -33202.4262 16740.1399  False
     8     11  18776.8846 0.4487    -7424.82 44978.5891  False
     8     12   4101.7367    0.9 -26077.4927 34280.9662  False
     9     10 -13908.9814    0.9 -44199.1905 16381.2276  False
     9     11  13099.0463    0.9 -18213.2693 44411.3619  False
     9     12  -1576.1016    0.9 -36285.1447 33132.9416  False
    10     11  27008.0277 0.0773  -1252.4216  55268.477  False
    10     12  12332.8799    0.9 -19650.0775 44315.8372  False
    11     12 -14675.1478    0.9 -47627.7487  18277.453  False
--------------------------------------------------------------


data.YrSold.isnull().sum()

0


data.YrSold.describe()

count    2073.000000
mean     2007.851905
std         1.329705
min      2006.000000
25%      2007.000000
50%      2008.000000
75%      2009.000000
max      2010.000000
Name: YrSold, dtype: float64


data.YrSold.value_counts()

2009    508
2007    469
2006    428
2008    413
2010    255
Name: YrSold, dtype: int64


data.YrSold.value_counts().sum()

2073


sns.countplot(data.YrSold)
plt.title('YrSold - count')

Text(0.5, 1.0, 'YrSold - count')


data.groupby("YrSold")["Property_Sale_Price"].describe()


mod_YS = ols('Property_Sale_Price ~ YrSold', data = data).fit()
sm.stats.anova_lm(mod_YS)


tukey_YS = pairwise_tukeyhsd(data.Property_Sale_Price, data.YrSold, alpha = 0.05)
print(tukey_YS)

    Multiple Comparison of Means - Tukey HSD, FWER=0.05     
============================================================
group1 group2  meandiff  p-adj    lower      upper    reject
------------------------------------------------------------
  2006   2007  1237.6607   0.9 -11005.8613 13481.1828  False
  2006   2008   1073.308   0.9 -11560.0714 13706.6875  False
  2006   2009 -1110.8867   0.9 -13128.0687 10906.2952  False
  2006   2010  1138.7192   0.9 -13350.2281 15627.6664  False
  2007   2008  -164.3527   0.9 -12523.5794  12194.874  False
  2007   2009 -2348.5475   0.9 -14077.1821  9380.0872  False
  2007   2010   -98.9416   0.9 -14349.4784 14151.5952  False
  2008   2009 -2184.1948   0.9 -14319.2396  9950.8501  False
  2008   2010    65.4111   0.9 -14521.4406 14652.2629  False
  2009   2010  2249.6059   0.9 -11806.9452  16306.157  False
------------------------------------------------------------


data.SaleType.isnull().sum()

0


data.SaleType.value_counts()

WD       1797
New       171
COD        68
ConLD      14
CWD         5
ConLI       5
ConLw       5
Oth         5
Con         3
Name: SaleType, dtype: int64


data.SaleType.value_counts().sum()

2073


sns.countplot(data.SaleType)
plt.title('SaleType - count')

Text(0.5, 1.0, 'SaleType - count')


data.groupby("SaleType")["Property_Sale_Price"].describe()


mod_ST = ols('Property_Sale_Price ~ SaleType', data = data).fit()
sm.stats.anova_lm(mod_ST)


tukey_ST = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleType, alpha = 0.05)
print(tukey_ST)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
group1 group2   meandiff   p-adj     lower        upper    reject
-----------------------------------------------------------------
   COD    CWD   79191.6176 0.1493  -11987.9012 170371.1365  False
   COD    Con   96398.2843 0.1961  -19690.2733 212486.8419  False
   COD  ConLD  -25066.3824    0.9  -82817.9499  32685.1852  False
   COD  ConLI   22931.6176    0.9  -68247.9012 114111.1365  False
   COD  ConLw  -11368.3824    0.9 -102547.9012  79811.1365  False
   COD    New   93001.4714  0.001   64790.2582 121212.6847   True
   COD    Oth  -23158.3824    0.9 -114337.9012  68021.1365  False
   COD     WD   16980.0456 0.4297   -7330.0266  41290.1177  False
   CWD    Con   17206.6667    0.9  -126499.263 160912.5963  False
   CWD  ConLD    -104258.0 0.0428 -206776.7034  -1739.2966   True
   CWD  ConLI     -56260.0 0.8986 -180712.9858  68192.9858  False
   CWD  ConLw     -90560.0 0.3691 -215012.9858  33892.9858  False
   CWD    New   13809.8538    0.9   -75468.999 103088.7066  False
   CWD    Oth    -102350.0 0.2072 -226802.9858  22102.9858  False
   CWD     WD  -62211.5721 0.4141 -150335.4656  25912.3215  False
   Con  ConLD -121464.6667 0.0656 -246656.2523    3726.919  False
   Con  ConLI  -73466.6667 0.7873 -217172.5963   70239.263  False
   Con  ConLw -107766.6667 0.3251 -251472.5963   35939.263  False
   Con    New   -3396.8129    0.9 -117998.5673 111204.9415  False
   Con    Oth -119556.6667  0.194 -263262.5963   24149.263  False
   Con     WD  -79418.2387 0.4297 -193122.5446  34286.0671  False
 ConLD  ConLI      47998.0 0.8683  -54520.7034 150516.7034  False
 ConLD  ConLw      13698.0    0.9  -88820.7034 116216.7034  False
 ConLD    New  118067.8538  0.001   63366.3689 172769.3387   True
 ConLD    Oth       1908.0    0.9 -100610.7034 104426.7034  False
 ConLD     WD   42046.4279 0.2457  -10749.0217  94841.8776  False
 ConLI  ConLw     -34300.0    0.9 -158752.9858  90152.9858  False
 ConLI    New   70069.8538 0.2644   -19208.999 159348.7066  False
 ConLI    Oth     -46090.0    0.9 -170542.9858  78362.9858  False
 ConLI     WD   -5951.5721    0.9  -94075.4656  82172.3215  False
 ConLw    New  104369.8538 0.0088    15091.001 193648.7066   True
 ConLw    Oth     -11790.0    0.9 -136242.9858 112662.9858  False
 ConLw     WD   28348.4279    0.9  -59775.4656 116472.3215  False
   New    Oth -116159.8538 0.0018 -205438.7066  -26881.001   True
   New     WD  -76021.4259  0.001  -91769.0767  -60273.775   True
   Oth     WD   40138.4279 0.8922  -47985.4656 128262.3215  False
-----------------------------------------------------------------


data["SaleType"].replace("ConLD", 'COD', inplace = True)
data["SaleType"].replace("ConLI", 'COD', inplace = True)
data["SaleType"].replace("ConLI", 'COD', inplace = True)
data["SaleType"].replace("Oth", 'COD', inplace = True)
data["SaleType"].replace("CWD", 'New', inplace = True)
data["SaleType"].replace("Con", 'New', inplace = True)


data.SaleType.value_counts()

WD       1797
New       179
COD        92
ConLw       5
Name: SaleType, dtype: int64


data.SaleType.value_counts().sum()

2073


mod_ST = ols('Property_Sale_Price ~ SaleType', data = data).fit()
sm.stats.anova_lm(mod_ST)


tukey_ST = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleType, alpha = 0.05)
print(tukey_ST)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
   COD  ConLw  -7541.6087    0.9 -82336.3581  67253.1407  False
   COD    New  96499.4248  0.001  75605.0983 117393.7513   True
   COD     WD  20806.8192 0.0115   3396.2814  38217.3571   True
 ConLw    New 104041.0335 0.0017  30189.1537 177892.9133   True
 ConLw     WD  28348.4279 0.7243 -44594.3859 101291.2418  False
   New     WD -75692.6056  0.001 -88458.6713 -62926.5398   True
---------------------------------------------------------------


data["SaleType"].replace("ConLw", 'COD', inplace = True)


data.SaleType.value_counts()

WD     1797
New     179
COD      97
Name: SaleType, dtype: int64


data.SaleType.value_counts().sum()

2073


mod_ST = ols('Property_Sale_Price ~ SaleType', data = data).fit()
sm.stats.anova_lm(mod_ST)


tukey_ST = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleType, alpha = 0.05)
print(tukey_ST)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
group1 group2   meandiff  p-adj     lower       upper    reject
---------------------------------------------------------------
   COD    New  96888.1675  0.001  78159.5701  115616.765   True
   COD     WD   21195.562 0.0038   5711.2049   36679.919   True
   New     WD -75692.6056  0.001 -87335.3674 -64049.8438   True
---------------------------------------------------------------


sns.countplot(x = 'SaleType', data = data)
plt.title('SaleType - count')

Text(0.5, 1.0, 'SaleType - count')


sns.catplot(x = 'SaleType', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of SaleType vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of SaleType vs Property_Sale_Price')


sns.boxplot(x = 'SaleType', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of SaleType vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot Of SaleType vs Property_Sale_Price')


data.SaleCondition.isnull().sum()

0


data.SaleCondition.value_counts()

Normal     1695
Partial     175
Abnorml     155
Family       24
Alloca       19
AdjLand       5
Name: SaleCondition, dtype: int64


data.SaleCondition.value_counts().sum()

2073


sns.countplot(data.SaleCondition)
plt.title('SaleCondition - count')

Text(0.5, 1.0, 'SaleCondition - count')


data.groupby("SaleCondition")["Property_Sale_Price"].describe()


mod_SC = ols('Property_Sale_Price ~ SaleCondition', data = data).fit()
sm.stats.anova_lm(mod_SC)


tukey_SC = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleCondition, alpha = 0.05)
print(tukey_SC)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05        
==================================================================
 group1  group2   meandiff  p-adj     lower        upper    reject
------------------------------------------------------------------
Abnorml AdjLand    -34399.8 0.8163 -116225.2672  47425.6672  False
Abnorml  Alloca  19220.4632 0.7856  -24553.0424  62993.9687  False
Abnorml  Family      1300.2    0.9   -38203.193   40803.593  False
Abnorml  Normal  31329.6224  0.001   16217.8809   46441.364   True
Abnorml Partial 102674.9029  0.001   82811.6007 122538.2051   True
AdjLand  Alloca  53620.2632 0.5332   -36895.282 144135.8083  False
AdjLand  Family     35700.0 0.8445  -52829.4425 124229.4425  False
AdjLand  Normal  65729.4224 0.1844  -14926.0725 146384.9174  False
AdjLand Partial 137074.7029  0.001   55395.4833 218753.9224   True
 Alloca  Family -17920.2632    0.9  -73221.0308  37380.5045  False
 Alloca  Normal  12109.1593    0.9  -29436.2566  53654.5751  False
 Alloca Partial  83454.4397  0.001   39954.9268 126953.9526   True
 Family  Normal  30029.4224 0.1887   -6989.7597  67048.6045  False
 Family Partial 101374.7029  0.001    62175.138 140574.2677   True
 Normal Partial  71345.2804  0.001   57046.5894  85643.9714   True
------------------------------------------------------------------


data["SaleCondition"].replace('Family', 'Abnorml', inplace = True)
data["SaleCondition"].replace('Alloca', 'Normal', inplace = True)


data.SaleCondition.value_counts()

Normal     1714
Abnorml     179
Partial     175
AdjLand       5
Name: SaleCondition, dtype: int64


data.SaleCondition.value_counts().sum()

2073


mod_SC = ols('Property_Sale_Price ~ SaleCondition', data = data).fit()
sm.stats.anova_lm(mod_SC)


tukey_SC = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleCondition, alpha = 0.05)
print(tukey_SC)

       Multiple Comparison of Means - Tukey HSD, FWER=0.05       
=================================================================
 group1  group2   meandiff  p-adj     lower       upper    reject
-----------------------------------------------------------------
Abnorml AdjLand -34574.1285 0.6071 -108154.142   39005.885  False
Abnorml  Normal  31021.0617  0.001  18274.1243  43767.9991   True
Abnorml Partial 102500.5744  0.001  85249.4042 119751.7446   True
AdjLand  Normal  65595.1902 0.0936  -7083.9861 138274.3665  False
AdjLand Partial 137074.7029  0.001   63471.842 210677.5637   True
 Normal Partial  71479.5127  0.001  58601.3468  84357.6785   True
-----------------------------------------------------------------


data["SaleCondition"].replace('AdjLand', 'Abnorml', inplace = True)


data.SaleCondition.value_counts()

Normal     1714
Abnorml     184
Partial     175
Name: SaleCondition, dtype: int64


data.SaleCondition.value_counts().sum()

2073


mod_SC = ols('Property_Sale_Price ~ SaleCondition', data = data).fit()
sm.stats.anova_lm(mod_SC)


tukey_SC = pairwise_tukeyhsd(data.Property_Sale_Price, data.SaleCondition, alpha = 0.05)
print(tukey_SC)

      Multiple Comparison of Means - Tukey HSD, FWER=0.05      
===============================================================
 group1  group2   meandiff  p-adj   lower       upper    reject
---------------------------------------------------------------
Abnorml  Normal  31960.5761 0.001 20475.3045  43445.8476   True
Abnorml Partial 103440.0887 0.001 87807.6486 119072.5288   True
 Normal Partial  71479.5127 0.001 59730.5633   83228.462   True
---------------------------------------------------------------


sns.countplot(x = 'SaleCondition', data = data)
plt.title('SaleCondition - count')

Text(0.5, 1.0, 'SaleCondition - count')


sns.catplot(x = 'SaleCondition', y = 'Property_Sale_Price', data = data)
plt.title('Catplot Of SaleCondition vs Property_Sale_Price')

Text(0.5, 1.0, 'Catplot Of SaleCondition vs Property_Sale_Price')


sns.boxplot(x = 'SaleCondition', y = 'Property_Sale_Price', data = data)
plt.title('Boxplot Of SaleCondition vs Property_Sale_Price')

Text(0.5, 1.0, 'Boxplot Of SaleCondition vs Property_Sale_Price')


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 80 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LotFrontage          2073 non-null   float64
 3   LotArea              2073 non-null   float64
 4   Road_Type            2073 non-null   object 
 5   Alley                2073 non-null   object 
 6   Property_Shape       2073 non-null   object 
 7   LandContour          2073 non-null   object 
 8   Utilities            2073 non-null   object 
 9   LotConfig            2073 non-null   object 
 10  LandSlope            2073 non-null   object 
 11  Neighborhood         2073 non-null   object 
 12  Condition1           2073 non-null   object 
 13  Condition2           2073 non-null   object 
 14  Dwelling_Type        2073 non-null   object 
 15  HouseStyle           2073 non-null   object 
 16  OverallQual          2073 non-null   int64  
 17  OverallCond          2073 non-null   int64  
 18  YearBuilt            2073 non-null   float64
 19  YearRemodAdd         2073 non-null   int64  
 20  RoofStyle            2073 non-null   object 
 21  RoofMatl             2073 non-null   object 
 22  Exterior1st          2073 non-null   object 
 23  Exterior2nd          2073 non-null   object 
 24  MasVnrType           2073 non-null   object 
 25  MasVnrArea           2073 non-null   float64
 26  ExterQual            2073 non-null   object 
 27  ExterCond            2073 non-null   object 
 28  Foundation           2073 non-null   object 
 29  BsmtQual             2073 non-null   object 
 30  BsmtCond             2073 non-null   object 
 31  BsmtExposure         2073 non-null   object 
 32  BsmtFinType1         2073 non-null   object 
 33  BsmtFinSF1           2073 non-null   float64
 34  BsmtFinType2         2073 non-null   object 
 35  BsmtFinSF2           2073 non-null   int64  
 36  BsmtUnfSF            2073 non-null   float64
 37  TotalBsmtSF          2073 non-null   float64
 38  Heating              2073 non-null   object 
 39  HeatingQC            2073 non-null   object 
 40  CentralAir           2073 non-null   object 
 41  Electrical           2073 non-null   object 
 42  FirstFlrSF           2073 non-null   float64
 43  SecondFlrSF          2073 non-null   float64
 44  LowQualFinSF         2073 non-null   int64  
 45  GrLivArea            2073 non-null   float64
 46  BsmtFullBath         2073 non-null   int64  
 47  BsmtHalfBath         2073 non-null   int64  
 48  FullBath             2073 non-null   int64  
 49  HalfBath             2073 non-null   int64  
 50  BedroomAbvGr         2073 non-null   int64  
 51  KitchenAbvGr         2073 non-null   int64  
 52  KitchenQual          2073 non-null   object 
 53  TotRmsAbvGrd         2073 non-null   float64
 54  Functional           2073 non-null   object 
 55  Fireplaces           2073 non-null   int64  
 56  FireplaceQu          2073 non-null   object 
 57  GarageType           2073 non-null   object 
 58  GarageYrBlt          2073 non-null   float64
 59  GarageFinish         2073 non-null   object 
 60  GarageCars           2073 non-null   int64  
 61  GarageArea           2073 non-null   float64
 62  GarageQual           2073 non-null   object 
 63  GarageCond           2073 non-null   object 
 64  PavedDrive           2073 non-null   object 
 65  WoodDeckSF           2073 non-null   float64
 66  OpenPorchSF          2073 non-null   float64
 67  EnclosedPorch        2073 non-null   int64  
 68  ThrSsnPorch          2073 non-null   int64  
 69  ScreenPorch          2073 non-null   int64  
 70  PoolArea             2073 non-null   int64  
 71  PoolQC               2073 non-null   object 
 72  Fence                2073 non-null   object 
 73  MiscFeature          2073 non-null   object 
 74  MiscVal              2073 non-null   int64  
 75  MoSold               2073 non-null   int64  
 76  YrSold               2073 non-null   int64  
 77  SaleType             2073 non-null   object 
 78  SaleCondition        2073 non-null   object 
 79  Property_Sale_Price  2073 non-null   float64
dtypes: float64(16), int64(21), object(43)
memory usage: 1.3+ MB


# Heat Map OF Corrrlation

plt.figure(figsize=(18,10))
sns.heatmap(data.corr(), annot=True, cmap='coolwarm')
plt.show()


data.corr()


model1 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LotFrontage + LotArea + Road_Type + Alley + Property_Shape + LandContour + Utilities + LotConfig + LandSlope + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + YearBuilt + YearRemodAdd + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + MasVnrArea + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinSF1 + BsmtFinType2 + BsmtFinSF2 + BsmtUnfSF + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + SecondFlrSF + LowQualFinSF + GrLivArea + BsmtFullBath + BsmtHalfBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + TotRmsAbvGrd + Functional + Fireplaces + FireplaceQu + GarageType + GarageYrBlt + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + WoodDeckSF + OpenPorchSF + EnclosedPorch + ThrSsnPorch + ScreenPorch + PoolArea + PoolQC + Fence + MiscFeature + MiscVal + MoSold + YrSold + SaleType + SaleCondition', data = data).fit()
print(model1.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.925
Model:                             OLS   Adj. R-squared:                  0.921
Method:                  Least Squares   F-statistic:                     196.3
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:37:22   Log-Likelihood:                -23289.
No. Observations:                 2073   AIC:                         4.683e+04
Df Residuals:                     1949   BIC:                         4.753e+04
Df Model:                          123                                         
Covariance Type:             nonrobust                                         
===============================================================================================
                                  coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------------------
Intercept                   -2.922e+05   7.02e+05     -0.416      0.677   -1.67e+06    1.08e+06
Zone_Class[T.FV]             3.004e+04   6890.418      4.360      0.000    1.65e+04    4.36e+04
Zone_Class[T.RL]              3.03e+04   6389.883      4.742      0.000    1.78e+04    4.28e+04
Zone_Class[T.RM]             2.682e+04   6215.736      4.315      0.000    1.46e+04     3.9e+04
Road_Type[T.Pave]            2.266e+04   8013.833      2.827      0.005    6939.181    3.84e+04
Alley[T.No alley access]     5939.6312   2575.209      2.306      0.021     889.178     1.1e+04
Property_Shape[T.Reg]        -793.3245   1016.990     -0.780      0.435   -2787.826    1201.177
LandContour[T.HLS]           3527.3205   2988.615      1.180      0.238   -2333.897    9388.538
LandContour[T.Lvl]           2623.6208   2360.162      1.112      0.266   -2005.086    7252.328
Utilities[T.NoSeWa]         -4.051e+04   1.24e+04     -3.273      0.001   -6.48e+04   -1.62e+04
LotConfig[T.Inside]         -1318.3897   1019.078     -1.294      0.196   -3316.988     680.208
LandSlope[T.Mod]             6570.5172   2445.424      2.687      0.007    1774.595    1.14e+04
LandSlope[T.Sev]            -4493.9128   5754.964     -0.781      0.435   -1.58e+04    6792.618
Neighborhood[T.Edwards]     -1.016e+04   1951.779     -5.208      0.000    -1.4e+04   -6336.306
Neighborhood[T.IDOTRR]      -1.339e+04   3314.098     -4.041      0.000   -1.99e+04   -6894.215
Neighborhood[T.NAmes]       -6741.4599   1639.565     -4.112      0.000   -9956.945   -3525.975
Neighborhood[T.NridgHt]      2.524e+04   2133.141     11.832      0.000    2.11e+04    2.94e+04
Neighborhood[T.Somerst]      1.418e+04   1804.307      7.857      0.000    1.06e+04    1.77e+04
Neighborhood[T.StoneBr]      2.344e+04   3268.112      7.171      0.000     1.7e+04    2.98e+04
Condition1[T.Norm]           9638.0329   1599.549      6.025      0.000    6501.027    1.28e+04
Condition1[T.PosN]           1.198e+04   3379.069      3.546      0.000    5353.934    1.86e+04
Condition2[T.Norm]          -2039.5224   5218.401     -0.391      0.696   -1.23e+04    8194.711
Condition2[T.PosN]          -3.726e+04   1.32e+04     -2.825      0.005   -6.31e+04   -1.14e+04
Dwelling_Type[T.TwnhsE]      8122.1229   2463.600      3.297      0.001    3290.556     1.3e+04
HouseStyle[T.1Story]         -353.6546   1792.591     -0.197      0.844   -3869.252    3161.943
HouseStyle[T.2Story]        -3521.4454   1849.121     -1.904      0.057   -7147.909     105.018
RoofStyle[T.Hip]              220.5785   1173.048      0.188      0.851   -2079.983    2521.139
RoofMatl[T.WdShngl]          1.513e+04   5297.784      2.856      0.004    4739.051    2.55e+04
Exterior1st[T.MetalSd]      -8690.5091   5246.895     -1.656      0.098    -1.9e+04    1599.606
Exterior1st[T.Plywood]      -2613.7207   5247.628     -0.498      0.618   -1.29e+04    7677.832
Exterior1st[T.VinylSd]       -1.16e+04   5523.238     -2.100      0.036   -2.24e+04    -765.633
Exterior2nd[T.HdBoard]       1825.1634   4011.607      0.455      0.649   -6042.327    9692.654
Exterior2nd[T.MetalSd]       6331.8090   4104.069      1.543      0.123   -1717.017    1.44e+04
Exterior2nd[T.VinylSd]       1.047e+04   4333.800      2.416      0.016    1969.257     1.9e+04
MasVnrType[T.None]           -602.3015   1604.159     -0.375      0.707   -3748.349    2543.746
MasVnrType[T.Stone]          3836.7822   1867.175      2.055      0.040     174.912    7498.652
ExterQual[T.Fa]              3344.8323   6918.471      0.483      0.629   -1.02e+04    1.69e+04
ExterQual[T.Gd]             -3011.7423   3023.325     -0.996      0.319   -8941.033    2917.549
ExterQual[T.TA]             -1.064e+04   3317.651     -3.206      0.001   -1.71e+04   -4129.916
ExterCond[T.TA]             -2001.5575   3657.794     -0.547      0.584   -9175.157    5172.042
Foundation[T.CBlock]         1809.9148   1913.567      0.946      0.344   -1942.937    5562.767
Foundation[T.PConc]          3101.6098   2126.787      1.458      0.145   -1069.407    7272.627
BsmtQual[T.Gd]              -1.111e+04   2165.086     -5.132      0.000   -1.54e+04   -6864.070
BsmtQual[T.No Basement]     -1.113e+04   3947.104     -2.819      0.005   -1.89e+04   -3385.028
BsmtQual[T.TA]              -1.113e+04   2605.954     -4.271      0.000   -1.62e+04   -6018.487
BsmtCond[T.No basement]     -1.401e+04   3420.880     -4.096      0.000   -2.07e+04   -7304.422
BsmtCond[T.TA]              -4356.8336   2059.992     -2.115      0.035   -8396.852    -316.815
BsmtExposure[T.Gd]           8149.2240   1870.512      4.357      0.000    4480.809    1.18e+04
BsmtExposure[T.No]          -3009.7047   1131.734     -2.659      0.008   -5229.240    -790.169
BsmtExposure[T.No Basement] -8811.1262   1.36e+04     -0.647      0.518   -3.55e+04    1.79e+04
BsmtFinType1[T.GLQ]          8974.6202   1656.024      5.419      0.000    5726.855    1.22e+04
BsmtFinType1[T.No basement] -3.824e+04   2.68e+04     -1.426      0.154   -9.08e+04    1.44e+04
BsmtFinType1[T.Unf]          2313.7241   1315.470      1.759      0.079    -266.152    4893.600
BsmtFinType2[T.Rec]          -7.05e+04   2.22e+04     -3.181      0.001   -1.14e+05    -2.7e+04
BsmtFinType2[T.Unf]         -6.615e+04   2.21e+04     -2.990      0.003    -1.1e+05   -2.28e+04
Heating[T.Grav]             -1846.8451   5044.417     -0.366      0.714   -1.17e+04    8046.174
HeatingQC[T.Fa]             -6829.9815   2995.802     -2.280      0.023   -1.27e+04    -954.669
HeatingQC[T.Gd]             -2889.7607   1375.105     -2.101      0.036   -5586.591    -192.930
HeatingQC[T.TA]             -3406.4659   1341.600     -2.539      0.011   -6037.587    -775.345
CentralAir[T.Y]              3927.8551   2283.046      1.720      0.086    -549.613    8405.323
Electrical[T.SBrkr]         -1917.1380   1823.734     -1.051      0.293   -5493.813    1659.536
KitchenQual[T.Fa]           -2.225e+04   4067.091     -5.471      0.000   -3.02e+04   -1.43e+04
KitchenQual[T.Gd]           -1.759e+04   2268.898     -7.755      0.000    -2.2e+04   -1.31e+04
KitchenQual[T.TA]           -2.228e+04   2533.917     -8.793      0.000   -2.73e+04   -1.73e+04
Functional[T.Typ]            1.767e+04   1947.336      9.072      0.000    1.38e+04    2.15e+04
FireplaceQu[T.Fa]           -4642.6378   4851.447     -0.957      0.339   -1.42e+04    4871.933
FireplaceQu[T.Gd]            1857.2395   3731.501      0.498      0.619   -5460.912    9175.391
FireplaceQu[T.No Fireplace]   -78.9906   4207.064     -0.019      0.985   -8329.808    8171.827
FireplaceQu[T.TA]             706.9982   3825.204      0.185      0.853   -6794.923    8208.920
GarageType[T.BuiltIn]       -1830.6139   2127.683     -0.860      0.390   -6003.387    2342.159
GarageType[T.Detchd]        -2210.6164   1429.655     -1.546      0.122   -5014.431     593.198
GarageType[T.No Garage]     -7544.8365   6453.830     -1.169      0.243   -2.02e+04    5112.298
GarageFinish[T.No Garage]    2.869e+04   7517.321      3.817      0.000     1.4e+04    4.34e+04
GarageFinish[T.RFn]         -4183.3853   1271.275     -3.291      0.001   -6676.587   -1690.184
GarageFinish[T.Unf]         -2070.0065   1589.576     -1.302      0.193   -5187.454    1047.441
GarageQual[T.No Garage]     -2.048e+04   5838.916     -3.508      0.000   -3.19e+04   -9029.653
GarageQual[T.TA]            -1.287e+04   5006.120     -2.570      0.010   -2.27e+04   -3050.233
GarageCond[T.TA]             4495.7326   3304.648      1.360      0.174   -1985.283     1.1e+04
PavedDrive[T.Y]               698.0717   1914.275      0.365      0.715   -3056.169    4452.312
PoolQC[T.Fa]                -2.012e+04   2.08e+04     -0.965      0.335    -6.1e+04    2.08e+04
PoolQC[T.Gd]                -6.351e+04   1.95e+04     -3.255      0.001   -1.02e+05   -2.52e+04
PoolQC[T.No Pool]            3.921e+05   4.78e+04      8.206      0.000    2.98e+05    4.86e+05
Fence[T.No Fence]            -683.9440   1325.728     -0.516      0.606   -3283.939    1916.051
MiscFeature[T.Shed]          4009.3063   2530.370      1.584      0.113    -953.210    8971.823
SaleType[T.New]              2.589e+04   6048.297      4.281      0.000     1.4e+04    3.78e+04
SaleType[T.WD]              -1397.2483   2264.712     -0.617      0.537   -5838.760    3044.263
SaleCondition[T.Normal]      9649.0395   1674.752      5.761      0.000    6364.546    1.29e+04
SaleCondition[T.Partial]    -1.406e+04   5881.145     -2.391      0.017   -2.56e+04   -2525.705
Dwell_Type                    -88.7100     21.591     -4.109      0.000    -131.054     -46.366
LotFrontage                    19.2300     29.319      0.656      0.512     -38.270      76.730
LotArea                         0.4907      0.157      3.130      0.002       0.183       0.798
OverallQual                  1979.5705    475.266      4.165      0.000    1047.488    2911.653
OverallCond                  2050.1630    815.184      2.515      0.012     451.438    3648.888
YearBuilt                       4.4822     25.090      0.179      0.858     -44.724      53.689
YearRemodAdd                  188.6820     33.569      5.621      0.000     122.847     254.517
MasVnrArea                      6.8752      5.882      1.169      0.243      -4.661      18.411
BsmtFinSF1                    -42.4461      9.412     -4.510      0.000     -60.905     -23.987
BsmtFinSF2                    -45.2815      9.932     -4.559      0.000     -64.759     -25.804
BsmtUnfSF                     -52.7726      9.554     -5.524      0.000     -71.509     -34.036
TotalBsmtSF                    74.3071      9.902      7.504      0.000      54.887      93.727
FirstFlrSF                    -15.9138      7.666     -2.076      0.038     -30.948      -0.880
SecondFlrSF                    -5.6401      6.947     -0.812      0.417     -19.265       7.984
LowQualFinSF                  -47.8284     11.111     -4.305      0.000     -69.619     -26.038
GrLivArea                      55.9784      7.291      7.678      0.000      41.680      70.277
BsmtFullBath                -1139.2707    648.018     -1.758      0.079   -2410.151     131.610
BsmtHalfBath                  558.9403   1979.837      0.282      0.778   -3323.881    4441.762
FullBath                     2735.1900   1428.585      1.915      0.056     -66.524    5536.904
HalfBath                     1944.8002   1381.014      1.408      0.159    -763.620    4653.221
BedroomAbvGr                   40.5214    915.816      0.044      0.965   -1755.561    1836.604
KitchenAbvGr                -1.866e+04   3311.227     -5.635      0.000   -2.52e+04   -1.22e+04
TotRmsAbvGrd                  404.1464    598.096      0.676      0.499    -768.829    1577.122
Fireplaces                   4734.3514   1693.539      2.796      0.005    1413.012    8055.690
GarageYrBlt                  -139.2414     36.092     -3.858      0.000    -210.025     -68.458
GarageCars                   3100.7897   1535.764      2.019      0.044      88.878    6112.702
GarageArea                     34.3182      5.243      6.546      0.000      24.036      44.600
WoodDeckSF                     17.7305      4.225      4.196      0.000       9.444      26.017
OpenPorchSF                    26.1744      9.890      2.647      0.008       6.778      45.570
EnclosedPorch                   6.5234      8.157      0.800      0.424      -9.475      22.522
ThrSsnPorch                    21.1005     16.094      1.311      0.190     -10.463      52.664
ScreenPorch                    35.7903      8.567      4.178      0.000      18.989      52.592
PoolArea                      698.0588     84.368      8.274      0.000     532.597     863.520
MiscVal                         1.1816      1.054      1.121      0.263      -0.886       3.249
MoSold                        119.1195    164.484      0.724      0.469    -203.463     441.702
YrSold                        -70.6403    347.145     -0.203      0.839    -751.454     610.173
==============================================================================
Omnibus:                      179.506   Durbin-Watson:                   2.049
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1055.473
Skew:                          -0.129   Prob(JB):                    6.41e-230
Kurtosis:                       6.486   Cond. No.                     1.91e+07
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.91e+07. This might indicate that there are
strong multicollinearity or other numerical problems.


model2 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + Road_Type + Alley + Property_Shape + LandContour + LotConfig + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinType2 + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + GrLivArea + BsmtFullBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + Functional + Fireplaces + FireplaceQu + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + Fence + MiscFeature + SaleType + SaleCondition', data = data).fit()
print(model2.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.915
Model:                             OLS   Adj. R-squared:                  0.911
Method:                  Least Squares   F-statistic:                     222.9
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:37:23   Log-Likelihood:                -23428.
No. Observations:                 2073   AIC:                         4.705e+04
Df Residuals:                     1977   BIC:                         4.759e+04
Df Model:                           95                                         
Covariance Type:             nonrobust                                         
===============================================================================================
                                  coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------------------
Intercept                    2.949e+04   2.73e+04      1.081      0.280    -2.4e+04     8.3e+04
Zone_Class[T.FV]             3.107e+04   7231.259      4.297      0.000    1.69e+04    4.53e+04
Zone_Class[T.RL]              3.11e+04   6714.572      4.632      0.000    1.79e+04    4.43e+04
Zone_Class[T.RM]             2.663e+04   6525.447      4.080      0.000    1.38e+04    3.94e+04
Road_Type[T.Pave]             1.35e+04   8354.476      1.616      0.106   -2880.769    2.99e+04
Alley[T.No alley access]     3874.4335   2689.289      1.441      0.150   -1399.704    9148.571
Property_Shape[T.Reg]       -1375.5514   1063.990     -1.293      0.196   -3462.211     711.108
LandContour[T.HLS]           8915.2545   3073.450      2.901      0.004    2887.714    1.49e+04
LandContour[T.Lvl]           4916.7337   2411.037      2.039      0.042     188.293    9645.175
LotConfig[T.Inside]         -1480.8200   1057.304     -1.401      0.162   -3554.367     592.727
Neighborhood[T.Edwards]     -8284.9469   2013.304     -4.115      0.000   -1.22e+04   -4336.526
Neighborhood[T.IDOTRR]      -1.371e+04   3458.817     -3.965      0.000   -2.05e+04   -6930.046
Neighborhood[T.NAmes]       -6008.2102   1709.653     -3.514      0.000   -9361.121   -2655.299
Neighborhood[T.NridgHt]      2.789e+04   2169.062     12.860      0.000    2.36e+04    3.21e+04
Neighborhood[T.Somerst]      1.499e+04   1880.285      7.974      0.000    1.13e+04    1.87e+04
Neighborhood[T.StoneBr]      2.644e+04   3369.340      7.848      0.000    1.98e+04    3.31e+04
Condition1[T.Norm]           1.064e+04   1681.972      6.325      0.000    7340.458    1.39e+04
Condition1[T.PosN]           1.492e+04   3540.865      4.212      0.000    7970.970    2.19e+04
Condition2[T.Norm]            -49.4815   5460.027     -0.009      0.993   -1.08e+04    1.07e+04
Condition2[T.PosN]          -4.295e+04   1.35e+04     -3.180      0.001   -6.94e+04   -1.65e+04
Dwelling_Type[T.TwnhsE]      1.024e+04   2553.144      4.012      0.000    5236.497    1.53e+04
HouseStyle[T.1Story]          619.3548   1873.886      0.331      0.741   -3055.643    4294.353
HouseStyle[T.2Story]        -3780.8137   1917.995     -1.971      0.049   -7542.318     -19.309
RoofStyle[T.Hip]             1690.4723   1226.274      1.379      0.168    -714.453    4095.398
RoofMatl[T.WdShngl]          1.042e+04   5474.374      1.904      0.057    -312.074    2.12e+04
Exterior1st[T.MetalSd]      -8235.5834   5535.797     -1.488      0.137   -1.91e+04    2621.026
Exterior1st[T.Plywood]      -1159.7488   5527.049     -0.210      0.834    -1.2e+04    9679.705
Exterior1st[T.VinylSd]      -8741.0645   5810.747     -1.504      0.133   -2.01e+04    2654.767
Exterior2nd[T.HdBoard]       3443.8812   4219.797      0.816      0.415   -4831.835    1.17e+04
Exterior2nd[T.MetalSd]       8510.7359   4316.030      1.972      0.049      46.290     1.7e+04
Exterior2nd[T.VinylSd]       1.087e+04   4550.212      2.389      0.017    1944.769    1.98e+04
MasVnrType[T.None]          -1274.2169   1183.501     -1.077      0.282   -3595.257    1046.823
MasVnrType[T.Stone]          3305.9509   1947.921      1.697      0.090    -514.242    7126.144
ExterQual[T.Fa]             -3828.9733   7220.285     -0.530      0.596    -1.8e+04    1.03e+04
ExterQual[T.Gd]             -4239.2877   3141.431     -1.349      0.177   -1.04e+04    1921.577
ExterQual[T.TA]             -1.089e+04   3457.092     -3.149      0.002   -1.77e+04   -4107.815
ExterCond[T.TA]               238.0023   3845.841      0.062      0.951   -7304.326    7780.330
Foundation[T.CBlock]         2437.3976   1976.318      1.233      0.218   -1438.487    6313.282
Foundation[T.PConc]          1020.8542   2194.331      0.465      0.642   -3282.591    5324.299
BsmtQual[T.Gd]              -1.285e+04   2265.686     -5.672      0.000   -1.73e+04   -8408.576
BsmtQual[T.No Basement]     -1.389e+04   4103.430     -3.386      0.001   -2.19e+04   -5845.956
BsmtQual[T.TA]              -1.432e+04   2692.971     -5.319      0.000   -1.96e+04   -9043.355
BsmtCond[T.No basement]     -1.469e+04   3564.657     -4.121      0.000   -2.17e+04   -7697.860
BsmtCond[T.TA]              -5513.2068   2137.403     -2.579      0.010   -9705.006   -1321.407
BsmtExposure[T.Gd]           9462.7377   1933.198      4.895      0.000    5671.417    1.33e+04
BsmtExposure[T.No]          -4748.1988   1174.409     -4.043      0.000   -7051.408   -2444.990
BsmtExposure[T.No Basement] -9863.3394   1.44e+04     -0.686      0.493    -3.8e+04    1.83e+04
BsmtFinType1[T.GLQ]          9513.3563   1668.536      5.702      0.000    6241.083    1.28e+04
BsmtFinType1[T.No basement]   1.91e+04   2.56e+04      0.746      0.456   -3.11e+04    6.93e+04
BsmtFinType1[T.Unf]           857.2812   1287.577      0.666      0.506   -1667.870    3382.432
BsmtFinType2[T.Rec]         -1.092e+04   2.04e+04     -0.537      0.592   -5.08e+04     2.9e+04
BsmtFinType2[T.Unf]         -7507.2070   2.03e+04     -0.370      0.711   -4.73e+04    3.23e+04
Heating[T.Grav]             -2326.7430   5322.299     -0.437      0.662   -1.28e+04    8111.161
HeatingQC[T.Fa]             -5947.4515   3109.931     -1.912      0.056    -1.2e+04     151.635
HeatingQC[T.Gd]             -1932.1179   1437.285     -1.344      0.179   -4750.870     886.634
HeatingQC[T.TA]             -3562.4604   1397.771     -2.549      0.011   -6303.720    -821.201
CentralAir[T.Y]              3923.9751   2401.788      1.634      0.102    -786.326    8634.276
Electrical[T.SBrkr]           588.2151   1876.008      0.314      0.754   -3090.945    4267.375
KitchenQual[T.Fa]           -2.619e+04   4234.564     -6.185      0.000   -3.45e+04   -1.79e+04
KitchenQual[T.Gd]           -1.773e+04   2389.152     -7.423      0.000   -2.24e+04    -1.3e+04
KitchenQual[T.TA]           -2.512e+04   2642.193     -9.507      0.000   -3.03e+04   -1.99e+04
Functional[T.Typ]            1.663e+04   2042.323      8.142      0.000    1.26e+04    2.06e+04
FireplaceQu[T.Fa]           -2555.9064   5074.945     -0.504      0.615   -1.25e+04    7396.897
FireplaceQu[T.Gd]            1823.0485   3874.968      0.470      0.638   -5776.402    9422.499
FireplaceQu[T.No Fireplace] -1014.8925   4399.602     -0.231      0.818   -9643.237    7613.452
FireplaceQu[T.TA]            1147.8511   3991.360      0.288      0.774   -6679.864    8975.566
GarageType[T.BuiltIn]       -2130.6148   2225.456     -0.957      0.338   -6495.101    2233.871
GarageType[T.Detchd]        -4455.8961   1470.281     -3.031      0.002   -7339.360   -1572.432
GarageType[T.No Garage]     -2759.4344   6761.041     -0.408      0.683    -1.6e+04    1.05e+04
GarageFinish[T.No Garage]    1.732e+04   7645.305      2.265      0.024    2325.558    3.23e+04
GarageFinish[T.RFn]         -3942.0785   1325.972     -2.973      0.003   -6542.527   -1341.630
GarageFinish[T.Unf]         -1393.2860   1668.825     -0.835      0.404   -4666.127    1879.554
GarageQual[T.No Garage]     -2.498e+04   5863.492     -4.261      0.000   -3.65e+04   -1.35e+04
GarageQual[T.TA]             -1.95e+04   4999.433     -3.901      0.000   -2.93e+04   -9696.464
GarageCond[T.TA]             4465.7450   3468.501      1.288      0.198   -2336.557    1.13e+04
PavedDrive[T.Y]              -881.8291   1995.314     -0.442      0.659   -4794.969    3031.311
Fence[T.No Fence]           -1201.9158   1384.764     -0.868      0.386   -3917.666    1513.834
MiscFeature[T.Shed]          4254.3337   2529.973      1.682      0.093    -707.359    9216.027
SaleType[T.New]              2.814e+04   6361.898      4.424      0.000    1.57e+04    4.06e+04
SaleType[T.WD]               1101.1544   2338.566      0.471      0.638   -3485.158    5687.467
SaleCondition[T.Normal]      9558.9590   1739.497      5.495      0.000    6147.519     1.3e+04
SaleCondition[T.Partial]    -1.499e+04   6192.042     -2.420      0.016   -2.71e+04   -2842.286
Dwell_Type                   -117.1537     21.813     -5.371      0.000    -159.932     -74.375
OverallQual                  1368.3789    496.475      2.756      0.006     394.709    2342.048
OverallCond                  2952.1808    833.812      3.541      0.000    1316.939    4587.422
TotalBsmtSF                    23.0983      2.929      7.887      0.000      17.354      28.842
FirstFlrSF                     -8.1891      4.149     -1.974      0.049     -16.326      -0.052
GrLivArea                      53.0754      2.807     18.907      0.000      47.570      58.581
BsmtFullBath                -3036.6573    557.987     -5.442      0.000   -4130.961   -1942.353
FullBath                     2717.4349   1476.396      1.841      0.066    -178.020    5612.890
HalfBath                     3908.6039   1417.090      2.758      0.006    1129.458    6687.750
BedroomAbvGr                  503.5063    926.248      0.544      0.587   -1313.018    2320.030
KitchenAbvGr                -1.662e+04   3410.508     -4.875      0.000   -2.33e+04   -9936.000
Fireplaces                   4498.6900   1744.577      2.579      0.010    1077.287    7920.093
GarageCars                   4183.3804   1600.502      2.614      0.009    1044.533    7322.227
GarageArea                     28.4207      5.282      5.381      0.000      18.062      38.780
==============================================================================
Omnibus:                      330.370   Durbin-Watson:                   2.036
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2963.683
Skew:                          -0.465   Prob(JB):                         0.00
Kurtosis:                       8.783   Cond. No.                     2.23e+05
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.23e+05. This might indicate that there are
strong multicollinearity or other numerical problems.


data.Condition2.value_counts()

Norm     2054
Feedr      16
PosN        3
Name: Condition2, dtype: int64


data["Condition2"].replace('Norm', 'Feedr', inplace = True)


data.Condition2.value_counts()

Feedr    2070
PosN        3
Name: Condition2, dtype: int64


data.HouseStyle.value_counts()

1Story    1125
2Story     648
1.5Fin     300
Name: HouseStyle, dtype: int64


data["HouseStyle"].replace('1Story', '1.5Fin', inplace = True)


data.HouseStyle.value_counts()

1.5Fin    1425
2Story     648
Name: HouseStyle, dtype: int64


data.Exterior2nd.value_counts()

VinylSd    864
HdBoard    581
MetalSd    576
AsbShng     52
Name: Exterior2nd, dtype: int64


data["Exterior2nd"].replace('HdBoard', 'AsbShng', inplace = True)
data["Exterior2nd"].replace('MetalSd', 'AsbShng', inplace = True)


data.Exterior2nd.value_counts()

AsbShng    1209
VinylSd     864
Name: Exterior2nd, dtype: int64


data.ExterQual.value_counts()

TA    1272
Gd     706
Ex      76
Fa      19
Name: ExterQual, dtype: int64


data["ExterQual"].replace('Gd', 'Ex', inplace = True)
data["ExterQual"].replace('Fa', 'Ex', inplace = True)


data.ExterQual.value_counts()

TA    1272
Ex     801
Name: ExterQual, dtype: int64


data.BsmtExposure.value_counts()

No             1349
Av              482
Gd              181
No Basement      61
Name: BsmtExposure, dtype: int64


data["BsmtExposure"].replace('No Basement', 'Av', inplace = True)


data.BsmtExposure.value_counts()

No    1349
Av     543
Gd     181
Name: BsmtExposure, dtype: int64


data.BsmtFinType1.value_counts()

Unf            923
GLQ            599
BLQ            492
No basement     59
Name: BsmtFinType1, dtype: int64


data["BsmtFinType1"].replace('Unf', 'BLQ', inplace = True)
data["BsmtFinType1"].replace('No basement', 'BLQ', inplace = True)


data.BsmtFinType1.value_counts()

BLQ    1474
GLQ     599
Name: BsmtFinType1, dtype: int64


data.HeatingQC.value_counts()

Ex    1045
TA     618
Gd     341
Fa      69
Name: HeatingQC, dtype: int64


data["HeatingQC"].replace('Gd', 'Ex', inplace = True)
data["HeatingQC"].replace('Fa', 'Ex', inplace = True)


data.HeatingQC.value_counts()

Ex    1455
TA     618
Name: HeatingQC, dtype: int64


data.GarageType.value_counts()

Attchd       1252
Detchd        572
No Garage     125
BuiltIn       124
Name: GarageType, dtype: int64


data["GarageType"].replace('No Garage', 'Attchd', inplace = True)
data["GarageType"].replace('BuiltIn', 'Attchd', inplace = True)


data.GarageType.value_counts()

Attchd    1501
Detchd     572
Name: GarageType, dtype: int64


data.GarageFinish.value_counts()

Unf          857
RFn          605
Fin          498
No Garage    113
Name: GarageFinish, dtype: int64


data["GarageFinish"].replace('Unf', 'Fin', inplace = True)


data.GarageFinish.value_counts()

Fin          1355
RFn           605
No Garage     113
Name: GarageFinish, dtype: int64


data.SaleType.value_counts()

WD     1797
New     179
COD      97
Name: SaleType, dtype: int64


data["SaleType"].replace('WD', 'COD', inplace = True)


data.SaleType.value_counts()

COD    1894
New     179
Name: SaleType, dtype: int64


model3 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + HeatingQC + FirstFlrSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition', data = data).fit()
print(model3.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.910
Model:                             OLS   Adj. R-squared:                  0.908
Method:                  Least Squares   F-statistic:                     402.8
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:37:24   Log-Likelihood:                -23477.
No. Observations:                 2073   AIC:                         4.706e+04
Df Residuals:                     2021   BIC:                         4.735e+04
Df Model:                           51                                         
Covariance Type:             nonrobust                                         
=============================================================================================
                                coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------------
Intercept                  3.879e+04   1.26e+04      3.074      0.002     1.4e+04    6.35e+04
Zone_Class[T.FV]           3.082e+04   6880.951      4.479      0.000    1.73e+04    4.43e+04
Zone_Class[T.RL]           2.999e+04   6363.279      4.713      0.000    1.75e+04    4.25e+04
Zone_Class[T.RM]           2.419e+04   6170.488      3.921      0.000    1.21e+04    3.63e+04
LandContour[T.HLS]         1.054e+04   3002.668      3.512      0.000    4655.454    1.64e+04
LandContour[T.Lvl]         6457.8894   2291.531      2.818      0.005    1963.881     1.1e+04
Neighborhood[T.Edwards]   -8833.5988   1914.853     -4.613      0.000   -1.26e+04   -5078.307
Neighborhood[T.IDOTRR]     -1.64e+04   3322.017     -4.938      0.000   -2.29e+04   -9889.062
Neighborhood[T.NAmes]     -6107.4222   1646.988     -3.708      0.000   -9337.394   -2877.451
Neighborhood[T.NridgHt]    2.855e+04   2125.881     13.432      0.000    2.44e+04    3.27e+04
Neighborhood[T.Somerst]    1.519e+04   1828.413      8.309      0.000    1.16e+04    1.88e+04
Neighborhood[T.StoneBr]    2.597e+04   3329.171      7.801      0.000    1.94e+04    3.25e+04
Condition1[T.Norm]         1.004e+04   1630.728      6.157      0.000    6842.233    1.32e+04
Condition1[T.PosN]         1.423e+04   3442.680      4.134      0.000    7481.969     2.1e+04
Condition2[T.PosN]        -3.878e+04   1.23e+04     -3.159      0.002   -6.29e+04   -1.47e+04
Dwelling_Type[T.TwnhsE]    9805.8697   2394.187      4.096      0.000    5110.538    1.45e+04
HouseStyle[T.2Story]      -4091.2054   1815.945     -2.253      0.024   -7652.524    -529.887
Exterior2nd[T.VinylSd]     4746.5916   1185.390      4.004      0.000    2421.877    7071.306
ExterQual[T.TA]           -7206.7484   1552.068     -4.643      0.000   -1.03e+04   -4162.929
BsmtQual[T.Gd]            -1.478e+04   2130.736     -6.936      0.000    -1.9e+04   -1.06e+04
BsmtQual[T.No Basement]   -1.262e+04   3789.732     -3.329      0.001      -2e+04   -5183.596
BsmtQual[T.TA]            -1.733e+04   2516.482     -6.885      0.000   -2.23e+04   -1.24e+04
BsmtCond[T.No basement]   -1.338e+04   3330.947     -4.016      0.000   -1.99e+04   -6843.759
BsmtCond[T.TA]            -5676.5872   2128.857     -2.666      0.008   -9851.572   -1501.603
BsmtExposure[T.Gd]         9679.7799   1909.610      5.069      0.000    5934.771    1.34e+04
BsmtExposure[T.No]        -4900.3797   1139.367     -4.301      0.000   -7134.835   -2665.924
BsmtFinType1[T.GLQ]        8559.1031   1310.466      6.531      0.000    5989.097    1.11e+04
HeatingQC[T.TA]           -2222.2766   1163.699     -1.910      0.056   -4504.451      59.898
KitchenQual[T.Fa]         -2.978e+04   3955.811     -7.528      0.000   -3.75e+04    -2.2e+04
KitchenQual[T.Gd]         -1.916e+04   2200.817     -8.708      0.000   -2.35e+04   -1.48e+04
KitchenQual[T.TA]         -2.699e+04   2487.125    -10.854      0.000   -3.19e+04   -2.21e+04
Functional[T.Typ]          1.753e+04   1983.331      8.837      0.000    1.36e+04    2.14e+04
GarageType[T.Detchd]      -5980.0935   1353.428     -4.418      0.000   -8634.353   -3325.834
GarageFinish[T.No Garage]   1.46e+04   3657.111      3.992      0.000    7427.597    2.18e+04
GarageFinish[T.RFn]       -3372.8364   1115.401     -3.024      0.003   -5560.293   -1185.380
GarageQual[T.No Garage]   -2.683e+04   5625.184     -4.770      0.000   -3.79e+04   -1.58e+04
GarageQual[T.TA]          -1.751e+04   4980.624     -3.515      0.000   -2.73e+04   -7737.901
SaleType[T.New]            2.543e+04   6052.840      4.201      0.000    1.36e+04    3.73e+04
SaleCondition[T.Normal]    9849.6247   1643.641      5.993      0.000    6626.217    1.31e+04
SaleCondition[T.Partial]  -1.296e+04   6207.761     -2.088      0.037   -2.51e+04    -787.892
Dwell_Type                 -112.6993     20.748     -5.432      0.000    -153.389     -72.010
OverallQual                1534.7792    496.069      3.094      0.002     561.919    2507.639
OverallCond                3260.4398    821.582      3.968      0.000    1649.204    4871.676
TotalBsmtSF                  19.3468      2.543      7.608      0.000      14.360      24.334
FirstFlrSF                   -1.2126      3.422     -0.354      0.723      -7.923       5.498
GrLivArea                    53.3255      2.096     25.442      0.000      49.215      57.436
BsmtFullBath              -2809.4724    543.779     -5.167      0.000   -3875.898   -1743.047
HalfBath                   3570.6588   1326.939      2.691      0.007     968.348    6172.970
KitchenAbvGr              -1.604e+04   3254.343     -4.930      0.000   -2.24e+04   -9660.974
Fireplaces                 6721.0056    899.675      7.470      0.000    4956.618    8485.394
GarageCars                 4702.5459   1571.593      2.992      0.003    1620.433    7784.658
GarageArea                   27.2543      5.244      5.198      0.000      16.971      37.538
==============================================================================
Omnibus:                      307.092   Durbin-Watson:                   2.034
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2460.583
Skew:                          -0.444   Prob(JB):                         0.00
Kurtosis:                       8.263   Cond. No.                     7.69e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 7.69e+04. This might indicate that there are
strong multicollinearity or other numerical problems.


model4 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition', data = data).fit()
print(model4.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.910
Model:                             OLS   Adj. R-squared:                  0.908
Method:                  Least Squares   F-statistic:                     418.8
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:37:25   Log-Likelihood:                -23479.
No. Observations:                 2073   AIC:                         4.706e+04
Df Residuals:                     2023   BIC:                         4.734e+04
Df Model:                           49                                         
Covariance Type:             nonrobust                                         
=============================================================================================
                                coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------------
Intercept                  3.608e+04   1.25e+04      2.879      0.004    1.15e+04    6.07e+04
Zone_Class[T.FV]           3.138e+04   6872.745      4.566      0.000    1.79e+04    4.49e+04
Zone_Class[T.RL]           3.041e+04   6359.595      4.782      0.000    1.79e+04    4.29e+04
Zone_Class[T.RM]           2.459e+04   6167.933      3.987      0.000    1.25e+04    3.67e+04
LandContour[T.HLS]         1.097e+04   2992.662      3.665      0.000    5099.098    1.68e+04
LandContour[T.Lvl]         6738.3304   2286.037      2.948      0.003    2255.097    1.12e+04
Neighborhood[T.Edwards]   -8739.9865   1907.686     -4.581      0.000   -1.25e+04   -4998.752
Neighborhood[T.IDOTRR]    -1.644e+04   3318.202     -4.955      0.000   -2.29e+04   -9935.001
Neighborhood[T.NAmes]     -6236.3190   1645.680     -3.790      0.000   -9463.724   -3008.914
Neighborhood[T.NridgHt]    2.868e+04   2125.698     13.491      0.000    2.45e+04    3.28e+04
Neighborhood[T.Somerst]    1.524e+04   1827.572      8.340      0.000    1.17e+04    1.88e+04
Neighborhood[T.StoneBr]    2.587e+04   3326.889      7.776      0.000    1.93e+04    3.24e+04
Condition1[T.Norm]         1.003e+04   1628.930      6.157      0.000    6834.137    1.32e+04
Condition1[T.PosN]         1.379e+04   3412.958      4.040      0.000    7096.664    2.05e+04
Condition2[T.PosN]        -3.823e+04   1.23e+04     -3.114      0.002   -6.23e+04   -1.42e+04
Dwelling_Type[T.TwnhsE]    1.015e+04   2386.389      4.252      0.000    5465.976    1.48e+04
HouseStyle[T.2Story]      -3751.9502   1558.980     -2.407      0.016   -6809.324    -694.576
Exterior2nd[T.VinylSd]     5185.6189   1164.479      4.453      0.000    2901.916    7469.321
ExterQual[T.TA]           -7532.9154   1540.588     -4.890      0.000   -1.06e+04   -4511.610
BsmtQual[T.Gd]            -1.504e+04   2126.608     -7.071      0.000   -1.92e+04   -1.09e+04
BsmtQual[T.No Basement]   -1.272e+04   3726.564     -3.412      0.001      -2e+04   -5407.513
BsmtQual[T.TA]            -1.746e+04   2516.623     -6.940      0.000   -2.24e+04   -1.25e+04
BsmtCond[T.No basement]   -1.372e+04   3315.858     -4.137      0.000   -2.02e+04   -7214.399
BsmtCond[T.TA]            -5672.8162   2129.787     -2.664      0.008   -9849.621   -1496.011
BsmtExposure[T.Gd]         9599.2630   1908.884      5.029      0.000    5855.678    1.33e+04
BsmtExposure[T.No]        -4835.7352   1138.506     -4.247      0.000   -7068.502   -2602.968
BsmtFinType1[T.GLQ]        8695.6846   1307.894      6.649      0.000    6130.724    1.13e+04
KitchenQual[T.Fa]         -3.011e+04   3948.766     -7.625      0.000   -3.79e+04   -2.24e+04
KitchenQual[T.Gd]         -1.896e+04   2198.724     -8.622      0.000   -2.33e+04   -1.46e+04
KitchenQual[T.TA]         -2.718e+04   2479.992    -10.960      0.000    -3.2e+04   -2.23e+04
Functional[T.Typ]          1.757e+04   1947.066      9.026      0.000    1.38e+04    2.14e+04
GarageType[T.Detchd]      -5865.3577   1346.444     -4.356      0.000   -8505.920   -3224.795
GarageFinish[T.No Garage]  1.459e+04   3655.976      3.990      0.000    7419.002    2.18e+04
GarageFinish[T.RFn]       -3329.4611   1113.804     -2.989      0.003   -5513.783   -1145.139
GarageQual[T.No Garage]   -2.626e+04   5620.039     -4.673      0.000   -3.73e+04   -1.52e+04
GarageQual[T.TA]          -1.723e+04   4979.857     -3.460      0.001    -2.7e+04   -7462.170
SaleType[T.New]            2.512e+04   6052.653      4.150      0.000    1.32e+04     3.7e+04
SaleCondition[T.Normal]    1.015e+04   1637.134      6.200      0.000    6939.803    1.34e+04
SaleCondition[T.Partial]  -1.237e+04   6202.942     -1.994      0.046   -2.45e+04    -202.534
Dwell_Type                 -112.2275     20.477     -5.481      0.000    -152.385     -72.070
OverallQual                1543.6254    495.948      3.112      0.002     571.004    2516.247
OverallCond                3317.3383    821.257      4.039      0.000    1706.740    4927.937
TotalBsmtSF                  18.7583      2.037      9.207      0.000      14.763      22.754
GrLivArea                    53.0167      1.738     30.511      0.000      49.609      56.424
BsmtFullBath              -2786.5927    543.881     -5.124      0.000   -3853.217   -1719.968
HalfBath                   3661.2747   1301.133      2.814      0.005    1109.573    6212.976
KitchenAbvGr              -1.617e+04   3252.289     -4.971      0.000   -2.25e+04   -9789.586
Fireplaces                 6655.6356    892.568      7.457      0.000    4905.187    8406.084
GarageCars                 4889.6647   1561.854      3.131      0.002    1826.654    7952.675
GarageArea                   26.1894      5.217      5.020      0.000      15.958      36.421
==============================================================================
Omnibus:                      305.507   Durbin-Watson:                   2.029
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2395.761
Skew:                          -0.448   Prob(JB):                         0.00
Kurtosis:                       8.190   Cond. No.                     6.54e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.54e+04. This might indicate that there are
strong multicollinearity or other numerical problems.


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 80 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LotFrontage          2073 non-null   float64
 3   LotArea              2073 non-null   float64
 4   Road_Type            2073 non-null   object 
 5   Alley                2073 non-null   object 
 6   Property_Shape       2073 non-null   object 
 7   LandContour          2073 non-null   object 
 8   Utilities            2073 non-null   object 
 9   LotConfig            2073 non-null   object 
 10  LandSlope            2073 non-null   object 
 11  Neighborhood         2073 non-null   object 
 12  Condition1           2073 non-null   object 
 13  Condition2           2073 non-null   object 
 14  Dwelling_Type        2073 non-null   object 
 15  HouseStyle           2073 non-null   object 
 16  OverallQual          2073 non-null   int64  
 17  OverallCond          2073 non-null   int64  
 18  YearBuilt            2073 non-null   float64
 19  YearRemodAdd         2073 non-null   int64  
 20  RoofStyle            2073 non-null   object 
 21  RoofMatl             2073 non-null   object 
 22  Exterior1st          2073 non-null   object 
 23  Exterior2nd          2073 non-null   object 
 24  MasVnrType           2073 non-null   object 
 25  MasVnrArea           2073 non-null   float64
 26  ExterQual            2073 non-null   object 
 27  ExterCond            2073 non-null   object 
 28  Foundation           2073 non-null   object 
 29  BsmtQual             2073 non-null   object 
 30  BsmtCond             2073 non-null   object 
 31  BsmtExposure         2073 non-null   object 
 32  BsmtFinType1         2073 non-null   object 
 33  BsmtFinSF1           2073 non-null   float64
 34  BsmtFinType2         2073 non-null   object 
 35  BsmtFinSF2           2073 non-null   int64  
 36  BsmtUnfSF            2073 non-null   float64
 37  TotalBsmtSF          2073 non-null   float64
 38  Heating              2073 non-null   object 
 39  HeatingQC            2073 non-null   object 
 40  CentralAir           2073 non-null   object 
 41  Electrical           2073 non-null   object 
 42  FirstFlrSF           2073 non-null   float64
 43  SecondFlrSF          2073 non-null   float64
 44  LowQualFinSF         2073 non-null   int64  
 45  GrLivArea            2073 non-null   float64
 46  BsmtFullBath         2073 non-null   int64  
 47  BsmtHalfBath         2073 non-null   int64  
 48  FullBath             2073 non-null   int64  
 49  HalfBath             2073 non-null   int64  
 50  BedroomAbvGr         2073 non-null   int64  
 51  KitchenAbvGr         2073 non-null   int64  
 52  KitchenQual          2073 non-null   object 
 53  TotRmsAbvGrd         2073 non-null   float64
 54  Functional           2073 non-null   object 
 55  Fireplaces           2073 non-null   int64  
 56  FireplaceQu          2073 non-null   object 
 57  GarageType           2073 non-null   object 
 58  GarageYrBlt          2073 non-null   float64
 59  GarageFinish         2073 non-null   object 
 60  GarageCars           2073 non-null   int64  
 61  GarageArea           2073 non-null   float64
 62  GarageQual           2073 non-null   object 
 63  GarageCond           2073 non-null   object 
 64  PavedDrive           2073 non-null   object 
 65  WoodDeckSF           2073 non-null   float64
 66  OpenPorchSF          2073 non-null   float64
 67  EnclosedPorch        2073 non-null   int64  
 68  ThrSsnPorch          2073 non-null   int64  
 69  ScreenPorch          2073 non-null   int64  
 70  PoolArea             2073 non-null   int64  
 71  PoolQC               2073 non-null   object 
 72  Fence                2073 non-null   object 
 73  MiscFeature          2073 non-null   object 
 74  MiscVal              2073 non-null   int64  
 75  MoSold               2073 non-null   int64  
 76  YrSold               2073 non-null   int64  
 77  SaleType             2073 non-null   object 
 78  SaleCondition        2073 non-null   object 
 79  Property_Sale_Price  2073 non-null   float64
dtypes: float64(16), int64(21), object(43)
memory usage: 1.3+ MB


data1 = data[['Dwell_Type', 'Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2', 'Dwelling_Type',
                   'HouseStyle', 'OverallQual', 'OverallCond', 'Exterior2nd', 'ExterQual', 'BsmtQual', 'BsmtCond',
                   'BsmtExposure', 'BsmtFinType1', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
                   'KitchenQual', 'Functional', 'Fireplaces', 'GarageType', 'GarageFinish', 'GarageCars', 'GarageArea',
                   'GarageQual', 'SaleType', 'SaleCondition', 'Property_Sale_Price']]
data1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 32 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LandContour          2073 non-null   object 
 3   Neighborhood         2073 non-null   object 
 4   Condition1           2073 non-null   object 
 5   Condition2           2073 non-null   object 
 6   Dwelling_Type        2073 non-null   object 
 7   HouseStyle           2073 non-null   object 
 8   OverallQual          2073 non-null   int64  
 9   OverallCond          2073 non-null   int64  
 10  Exterior2nd          2073 non-null   object 
 11  ExterQual            2073 non-null   object 
 12  BsmtQual             2073 non-null   object 
 13  BsmtCond             2073 non-null   object 
 14  BsmtExposure         2073 non-null   object 
 15  BsmtFinType1         2073 non-null   object 
 16  TotalBsmtSF          2073 non-null   float64
 17  GrLivArea            2073 non-null   float64
 18  BsmtFullBath         2073 non-null   int64  
 19  HalfBath             2073 non-null   int64  
 20  KitchenAbvGr         2073 non-null   int64  
 21  KitchenQual          2073 non-null   object 
 22  Functional           2073 non-null   object 
 23  Fireplaces           2073 non-null   int64  
 24  GarageType           2073 non-null   object 
 25  GarageFinish         2073 non-null   object 
 26  GarageCars           2073 non-null   int64  
 27  GarageArea           2073 non-null   float64
 28  GarageQual           2073 non-null   object 
 29  SaleType             2073 non-null   object 
 30  SaleCondition        2073 non-null   object 
 31  Property_Sale_Price  2073 non-null   float64
dtypes: float64(4), int64(8), object(20)
memory usage: 518.4+ KB


# these are predicted values, called y hat also

predict = model4.predict()
predict.shape

(2073,)


# Value Count Of Target Variable

data1.Property_Sale_Price.value_counts().sum()

2073


# Residuals = Actual(y)  - Predictor(y hat)

residuals = data1.Property_Sale_Price - predict
residuals

0        6090.577315
1      -10542.106317
2        2798.039754
3      -60127.286247
4      -37848.254893
            ...     
2068   -11960.633495
2069    18238.107138
2070   -14212.363893
2071   -37269.519371
2072   -39547.423624
Name: Property_Sale_Price, Length: 2073, dtype: float64


# Now We have a new column in our data

data1['predict'] = predict


# Now We have an another new column in our data

data1['residuals'] = residuals


# Now We Create Observation Number

obs = np.arange(2073) 
obs 

obsno = pd.DataFrame(obs) # convert array into a data frame 

data1['obsno'] = obsno # craete a new variable/column 'obsno' in yr data


data1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 35 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LandContour          2073 non-null   object 
 3   Neighborhood         2073 non-null   object 
 4   Condition1           2073 non-null   object 
 5   Condition2           2073 non-null   object 
 6   Dwelling_Type        2073 non-null   object 
 7   HouseStyle           2073 non-null   object 
 8   OverallQual          2073 non-null   int64  
 9   OverallCond          2073 non-null   int64  
 10  Exterior2nd          2073 non-null   object 
 11  ExterQual            2073 non-null   object 
 12  BsmtQual             2073 non-null   object 
 13  BsmtCond             2073 non-null   object 
 14  BsmtExposure         2073 non-null   object 
 15  BsmtFinType1         2073 non-null   object 
 16  TotalBsmtSF          2073 non-null   float64
 17  GrLivArea            2073 non-null   float64
 18  BsmtFullBath         2073 non-null   int64  
 19  HalfBath             2073 non-null   int64  
 20  KitchenAbvGr         2073 non-null   int64  
 21  KitchenQual          2073 non-null   object 
 22  Functional           2073 non-null   object 
 23  Fireplaces           2073 non-null   int64  
 24  GarageType           2073 non-null   object 
 25  GarageFinish         2073 non-null   object 
 26  GarageCars           2073 non-null   int64  
 27  GarageArea           2073 non-null   float64
 28  GarageQual           2073 non-null   object 
 29  SaleType             2073 non-null   object 
 30  SaleCondition        2073 non-null   object 
 31  Property_Sale_Price  2073 non-null   float64
 32  predict              2073 non-null   float64
 33  residuals            2073 non-null   float64
 34  obsno                2073 non-null   int32  
dtypes: float64(6), int32(1), int64(8), object(20)
memory usage: 558.9+ KB


plt.figure(figsize=(12,10))
sns.distplot(data1.residuals, color='skyblue')
plt.title('Histogram of Residuals')
plt.ylabel('Prob')
plt.xlabel('Residuals')
plt.show()


import math
data1['residuals'] = np.sqrt(data1.residuals)


plt.figure(figsize=(12,10))
sns.distplot(data1.residuals, color='skyblue')
plt.title('Histogram of Residuals')
plt.ylabel('Prob')
plt.xlabel('Residuals')
plt.show()


data1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 35 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LandContour          2073 non-null   object 
 3   Neighborhood         2073 non-null   object 
 4   Condition1           2073 non-null   object 
 5   Condition2           2073 non-null   object 
 6   Dwelling_Type        2073 non-null   object 
 7   HouseStyle           2073 non-null   object 
 8   OverallQual          2073 non-null   int64  
 9   OverallCond          2073 non-null   int64  
 10  Exterior2nd          2073 non-null   object 
 11  ExterQual            2073 non-null   object 
 12  BsmtQual             2073 non-null   object 
 13  BsmtCond             2073 non-null   object 
 14  BsmtExposure         2073 non-null   object 
 15  BsmtFinType1         2073 non-null   object 
 16  TotalBsmtSF          2073 non-null   float64
 17  GrLivArea            2073 non-null   float64
 18  BsmtFullBath         2073 non-null   int64  
 19  HalfBath             2073 non-null   int64  
 20  KitchenAbvGr         2073 non-null   int64  
 21  KitchenQual          2073 non-null   object 
 22  Functional           2073 non-null   object 
 23  Fireplaces           2073 non-null   int64  
 24  GarageType           2073 non-null   object 
 25  GarageFinish         2073 non-null   object 
 26  GarageCars           2073 non-null   int64  
 27  GarageArea           2073 non-null   float64
 28  GarageQual           2073 non-null   object 
 29  SaleType             2073 non-null   object 
 30  SaleCondition        2073 non-null   object 
 31  Property_Sale_Price  2073 non-null   float64
 32  predict              2073 non-null   float64
 33  residuals            1057 non-null   float64
 34  obsno                2073 non-null   int32  
dtypes: float64(6), int32(1), int64(8), object(20)
memory usage: 558.9+ KB


numerical_data1 = data1.select_dtypes(include=np.number)
print(len(numerical_data1.columns))
numerical_data1.columns

15

Index(['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea',
       'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 'Fireplaces', 'GarageCars',
       'GarageArea', 'Property_Sale_Price', 'predict', 'residuals', 'obsno'],
      dtype='object')


categorical_data1 = data1.select_dtypes(include = 'O')
print(len(categorical_data1.columns))
categorical_data1.columns

20

Index(['Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2',
       'Dwelling_Type', 'HouseStyle', 'Exterior2nd', 'ExterQual', 'BsmtQual',
       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'KitchenQual', 'Functional',
       'GarageType', 'GarageFinish', 'GarageQual', 'SaleType',
       'SaleCondition'],
      dtype='object')


### With Good Predictors

a = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 
     'Fireplaces', 'GarageCars', 'GarageArea', 'Property_Sale_Price', 'predict', 'residuals', 'obsno']


data1_pp = data1[a]


sns.pairplot(data1_pp ,palette = sns.color_palette('Set3_r'), markers = ['>'], plot_kws = {'facecolors':'#00FF00'})

<seaborn.axisgrid.PairGrid at 0x29b834777c0>


# visualize the relationship between the features and the response using scatterplots

sns.pairplot(data1, x_vars = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 
     'Fireplaces', 'GarageCars', 'GarageArea', 'predict', 'residuals', 'obsno'],
             y_vars = 'Property_Sale_Price', size=7, aspect=0.7)

<seaborn.axisgrid.PairGrid at 0x29b8c57bdf0>


plt.figure(figsize=(12,10))
sns.scatterplot(x = data1.obsno, y = data1.residuals, palette='spring_r', color='darkslateblue', markers='+')
plt.title('Scatterplot of obsno vs residuals')

Text(0.5, 1.0, 'Scatterplot of obsno vs residuals')


plt.figure(figsize=(12,10))
sns.scatterplot(x = data1.predict, y = data1.residuals, palette='spring', color='teal')
plt.title('Scatterplot of predict vs residuals')

Text(0.5, 1.0, 'Scatterplot of predict vs residuals')


data1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2073 entries, 0 to 2072
Data columns (total 35 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           2073 non-null   int64  
 1   Zone_Class           2073 non-null   object 
 2   LandContour          2073 non-null   object 
 3   Neighborhood         2073 non-null   object 
 4   Condition1           2073 non-null   object 
 5   Condition2           2073 non-null   object 
 6   Dwelling_Type        2073 non-null   object 
 7   HouseStyle           2073 non-null   object 
 8   OverallQual          2073 non-null   int64  
 9   OverallCond          2073 non-null   int64  
 10  Exterior2nd          2073 non-null   object 
 11  ExterQual            2073 non-null   object 
 12  BsmtQual             2073 non-null   object 
 13  BsmtCond             2073 non-null   object 
 14  BsmtExposure         2073 non-null   object 
 15  BsmtFinType1         2073 non-null   object 
 16  TotalBsmtSF          2073 non-null   float64
 17  GrLivArea            2073 non-null   float64
 18  BsmtFullBath         2073 non-null   int64  
 19  HalfBath             2073 non-null   int64  
 20  KitchenAbvGr         2073 non-null   int64  
 21  KitchenQual          2073 non-null   object 
 22  Functional           2073 non-null   object 
 23  Fireplaces           2073 non-null   int64  
 24  GarageType           2073 non-null   object 
 25  GarageFinish         2073 non-null   object 
 26  GarageCars           2073 non-null   int64  
 27  GarageArea           2073 non-null   float64
 28  GarageQual           2073 non-null   object 
 29  SaleType             2073 non-null   object 
 30  SaleCondition        2073 non-null   object 
 31  Property_Sale_Price  2073 non-null   float64
 32  predict              2073 non-null   float64
 33  residuals            1057 non-null   float64
 34  obsno                2073 non-null   int32  
dtypes: float64(6), int32(1), int64(8), object(20)
memory usage: 558.9+ KB


x = data1[['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 
            'KitchenAbvGr', 'Fireplaces', 'GarageCars', 'GarageArea']]


x.columns

Index(['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea',
       'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 'Fireplaces', 'GarageCars',
       'GarageArea'],
      dtype='object')


vif_data = pd.DataFrame()
vif_data["feature"] = x.columns


# calculating VIF for each feature

from statsmodels.stats.outliers_influence import variance_inflation_factor

vif_data["VIF"] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]
  
print(vif_data)

         feature        VIF
0     Dwell_Type   4.087129
1    OverallQual  35.242275
2    OverallCond  35.294751
3    TotalBsmtSF  13.048126
4      GrLivArea  24.222567
5   BsmtFullBath   6.411239
6       HalfBath   2.436959
7   KitchenAbvGr  23.739868
8     Fireplaces   2.695333
9     GarageCars  36.031353
10    GarageArea  32.552027


# import numpy and statsmodels for durbin watson

import numpy as np
from statsmodels.stats.stattools import durbin_watson

gfg = durbin_watson(model4.resid)


print("Durbin Watson Value :", round(gfg, 3))

Durbin Watson Value : 2.029


model5 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFullBath + HalfBath + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageQual + SaleType + SaleCondition', data = data1).fit()
print(model5.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.810
Model:                             OLS   Adj. R-squared:                  0.806
Method:                  Least Squares   F-statistic:                     205.6
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:38:49   Log-Likelihood:                -24258.
No. Observations:                 2073   AIC:                         4.860e+04
Df Residuals:                     2030   BIC:                         4.884e+04
Df Model:                           42                                         
Covariance Type:             nonrobust                                         
=============================================================================================
                                coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------------
Intercept                  2.339e+05   1.41e+04     16.613      0.000    2.06e+05    2.62e+05
Zone_Class[T.FV]           1.119e+04   9919.177      1.129      0.259   -8258.353    3.06e+04
Zone_Class[T.RL]            2.25e+04   9196.804      2.447      0.014    4467.158    4.05e+04
Zone_Class[T.RM]           1.513e+04   8909.359      1.698      0.090   -2341.613    3.26e+04
LandContour[T.HLS]         6554.2324   4301.927      1.524      0.128   -1882.420     1.5e+04
LandContour[T.Lvl]         1801.2449   3283.982      0.548      0.583   -4639.082    8241.571
Neighborhood[T.Edwards]    -1.32e+04   2746.788     -4.804      0.000   -1.86e+04   -7808.354
Neighborhood[T.IDOTRR]    -3.606e+04   4704.742     -7.665      0.000   -4.53e+04   -2.68e+04
Neighborhood[T.NAmes]     -9655.5830   2380.961     -4.055      0.000   -1.43e+04   -4986.201
Neighborhood[T.NridgHt]    4.941e+04   2997.896     16.482      0.000    4.35e+04    5.53e+04
Neighborhood[T.Somerst]    2.141e+04   2638.714      8.115      0.000    1.62e+04    2.66e+04
Neighborhood[T.StoneBr]    3.796e+04   4803.678      7.902      0.000    2.85e+04    4.74e+04
Condition1[T.Norm]         3393.9040   2348.683      1.445      0.149   -1212.175    7999.983
Condition1[T.PosN]          2.12e+04   4933.293      4.297      0.000    1.15e+04    3.09e+04
Condition2[T.PosN]        -1788.1958   1.77e+04     -0.101      0.919   -3.65e+04    3.29e+04
Dwelling_Type[T.TwnhsE]    6071.4912   2555.261      2.376      0.018    1060.284    1.11e+04
HouseStyle[T.2Story]       1.336e+04   1887.485      7.080      0.000    9660.955    1.71e+04
Exterior2nd[T.VinylSd]     6744.2790   1680.232      4.014      0.000    3449.120       1e+04
ExterQual[T.TA]           -1.589e+04   2195.229     -7.237      0.000   -2.02e+04   -1.16e+04
BsmtQual[T.Gd]            -3.083e+04   3041.206    -10.138      0.000   -3.68e+04   -2.49e+04
BsmtQual[T.No Basement]   -4.374e+04   5143.894     -8.504      0.000   -5.38e+04   -3.37e+04
BsmtQual[T.TA]            -3.617e+04   3553.895    -10.177      0.000   -4.31e+04   -2.92e+04
BsmtCond[T.No basement]   -1.705e+04   4708.665     -3.621      0.000   -2.63e+04   -7814.258
BsmtCond[T.TA]            -7894.9289   3090.275     -2.555      0.011    -1.4e+04   -1834.488
BsmtExposure[T.Gd]         1.676e+04   2744.060      6.109      0.000    1.14e+04    2.21e+04
BsmtExposure[T.No]        -3952.1219   1641.972     -2.407      0.016   -7172.247    -731.996
BsmtFinType1[T.GLQ]        1.026e+04   1893.772      5.420      0.000    6550.207     1.4e+04
KitchenQual[T.Fa]         -5.267e+04   5686.344     -9.262      0.000   -6.38e+04   -4.15e+04
KitchenQual[T.Gd]         -2.699e+04   3171.736     -8.508      0.000   -3.32e+04   -2.08e+04
KitchenQual[T.TA]         -4.336e+04   3554.448    -12.200      0.000   -5.03e+04   -3.64e+04
Functional[T.Typ]          4239.5977   2752.587      1.540      0.124   -1158.592    9637.788
GarageType[T.Detchd]      -9396.7787   1929.507     -4.870      0.000   -1.32e+04   -5612.759
GarageFinish[T.No Garage] -8206.9863   4983.919     -1.647      0.100    -1.8e+04    1567.142
GarageFinish[T.RFn]       -2599.7181   1605.160     -1.620      0.105   -5747.650     548.214
GarageQual[T.No Garage]   -3.662e+04   8084.043     -4.530      0.000   -5.25e+04   -2.08e+04
GarageQual[T.TA]          -2.504e+04   7186.063     -3.484      0.001   -3.91e+04   -1.09e+04
SaleType[T.New]            3.294e+04   8784.680      3.750      0.000    1.57e+04    5.02e+04
SaleCondition[T.Normal]    9088.0610   2370.303      3.834      0.000    4439.581    1.37e+04
SaleCondition[T.Partial]  -2.026e+04   8997.612     -2.252      0.024   -3.79e+04   -2614.387
Dwell_Type                 -225.6453     29.135     -7.745      0.000    -282.784    -168.507
BsmtFullBath              -1719.4163    776.919     -2.213      0.027   -3243.058    -195.775
HalfBath                   8354.9676   1808.244      4.620      0.000    4808.760    1.19e+04
Fireplaces                 1.979e+04   1210.433     16.350      0.000    1.74e+04    2.22e+04
==============================================================================
Omnibus:                      114.322   Durbin-Watson:                   2.008
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              282.668
Skew:                           0.310   Prob(JB):                     4.16e-62
Kurtosis:                       4.699   Cond. No.                     1.45e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.45e+03. This might indicate that there are
strong multicollinearity or other numerical problems.


from sklearn.metrics import mean_squared_error

MSE = mean_squared_error(data1.Property_Sale_Price ,data1.predict)
print("Mean Squared Error (MSE) :", round(MSE, 3))

Mean Squared Error (MSE) : 402937930.219


RMSE = np.sqrt(MSE)
print("Root Mean Squared Error (RMSE) :", round(RMSE, 3))

Root Mean Squared Error (RMSE) : 20073.314


# Split DAta into train and test in approx 80:20 ratio

trn = data.iloc[0 : 1660, ]
trn.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1660 entries, 0 to 1659
Data columns (total 80 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           1660 non-null   int64  
 1   Zone_Class           1660 non-null   object 
 2   LotFrontage          1660 non-null   float64
 3   LotArea              1660 non-null   float64
 4   Road_Type            1660 non-null   object 
 5   Alley                1660 non-null   object 
 6   Property_Shape       1660 non-null   object 
 7   LandContour          1660 non-null   object 
 8   Utilities            1660 non-null   object 
 9   LotConfig            1660 non-null   object 
 10  LandSlope            1660 non-null   object 
 11  Neighborhood         1660 non-null   object 
 12  Condition1           1660 non-null   object 
 13  Condition2           1660 non-null   object 
 14  Dwelling_Type        1660 non-null   object 
 15  HouseStyle           1660 non-null   object 
 16  OverallQual          1660 non-null   int64  
 17  OverallCond          1660 non-null   int64  
 18  YearBuilt            1660 non-null   float64
 19  YearRemodAdd         1660 non-null   int64  
 20  RoofStyle            1660 non-null   object 
 21  RoofMatl             1660 non-null   object 
 22  Exterior1st          1660 non-null   object 
 23  Exterior2nd          1660 non-null   object 
 24  MasVnrType           1660 non-null   object 
 25  MasVnrArea           1660 non-null   float64
 26  ExterQual            1660 non-null   object 
 27  ExterCond            1660 non-null   object 
 28  Foundation           1660 non-null   object 
 29  BsmtQual             1660 non-null   object 
 30  BsmtCond             1660 non-null   object 
 31  BsmtExposure         1660 non-null   object 
 32  BsmtFinType1         1660 non-null   object 
 33  BsmtFinSF1           1660 non-null   float64
 34  BsmtFinType2         1660 non-null   object 
 35  BsmtFinSF2           1660 non-null   int64  
 36  BsmtUnfSF            1660 non-null   float64
 37  TotalBsmtSF          1660 non-null   float64
 38  Heating              1660 non-null   object 
 39  HeatingQC            1660 non-null   object 
 40  CentralAir           1660 non-null   object 
 41  Electrical           1660 non-null   object 
 42  FirstFlrSF           1660 non-null   float64
 43  SecondFlrSF          1660 non-null   float64
 44  LowQualFinSF         1660 non-null   int64  
 45  GrLivArea            1660 non-null   float64
 46  BsmtFullBath         1660 non-null   int64  
 47  BsmtHalfBath         1660 non-null   int64  
 48  FullBath             1660 non-null   int64  
 49  HalfBath             1660 non-null   int64  
 50  BedroomAbvGr         1660 non-null   int64  
 51  KitchenAbvGr         1660 non-null   int64  
 52  KitchenQual          1660 non-null   object 
 53  TotRmsAbvGrd         1660 non-null   float64
 54  Functional           1660 non-null   object 
 55  Fireplaces           1660 non-null   int64  
 56  FireplaceQu          1660 non-null   object 
 57  GarageType           1660 non-null   object 
 58  GarageYrBlt          1660 non-null   float64
 59  GarageFinish         1660 non-null   object 
 60  GarageCars           1660 non-null   int64  
 61  GarageArea           1660 non-null   float64
 62  GarageQual           1660 non-null   object 
 63  GarageCond           1660 non-null   object 
 64  PavedDrive           1660 non-null   object 
 65  WoodDeckSF           1660 non-null   float64
 66  OpenPorchSF          1660 non-null   float64
 67  EnclosedPorch        1660 non-null   int64  
 68  ThrSsnPorch          1660 non-null   int64  
 69  ScreenPorch          1660 non-null   int64  
 70  PoolArea             1660 non-null   int64  
 71  PoolQC               1660 non-null   object 
 72  Fence                1660 non-null   object 
 73  MiscFeature          1660 non-null   object 
 74  MiscVal              1660 non-null   int64  
 75  MoSold               1660 non-null   int64  
 76  YrSold               1660 non-null   int64  
 77  SaleType             1660 non-null   object 
 78  SaleCondition        1660 non-null   object 
 79  Property_Sale_Price  1660 non-null   float64
dtypes: float64(16), int64(21), object(43)
memory usage: 1.0+ MB


test = data.iloc[1660: , ]
test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 413 entries, 1660 to 2072
Data columns (total 80 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           413 non-null    int64  
 1   Zone_Class           413 non-null    object 
 2   LotFrontage          413 non-null    float64
 3   LotArea              413 non-null    float64
 4   Road_Type            413 non-null    object 
 5   Alley                413 non-null    object 
 6   Property_Shape       413 non-null    object 
 7   LandContour          413 non-null    object 
 8   Utilities            413 non-null    object 
 9   LotConfig            413 non-null    object 
 10  LandSlope            413 non-null    object 
 11  Neighborhood         413 non-null    object 
 12  Condition1           413 non-null    object 
 13  Condition2           413 non-null    object 
 14  Dwelling_Type        413 non-null    object 
 15  HouseStyle           413 non-null    object 
 16  OverallQual          413 non-null    int64  
 17  OverallCond          413 non-null    int64  
 18  YearBuilt            413 non-null    float64
 19  YearRemodAdd         413 non-null    int64  
 20  RoofStyle            413 non-null    object 
 21  RoofMatl             413 non-null    object 
 22  Exterior1st          413 non-null    object 
 23  Exterior2nd          413 non-null    object 
 24  MasVnrType           413 non-null    object 
 25  MasVnrArea           413 non-null    float64
 26  ExterQual            413 non-null    object 
 27  ExterCond            413 non-null    object 
 28  Foundation           413 non-null    object 
 29  BsmtQual             413 non-null    object 
 30  BsmtCond             413 non-null    object 
 31  BsmtExposure         413 non-null    object 
 32  BsmtFinType1         413 non-null    object 
 33  BsmtFinSF1           413 non-null    float64
 34  BsmtFinType2         413 non-null    object 
 35  BsmtFinSF2           413 non-null    int64  
 36  BsmtUnfSF            413 non-null    float64
 37  TotalBsmtSF          413 non-null    float64
 38  Heating              413 non-null    object 
 39  HeatingQC            413 non-null    object 
 40  CentralAir           413 non-null    object 
 41  Electrical           413 non-null    object 
 42  FirstFlrSF           413 non-null    float64
 43  SecondFlrSF          413 non-null    float64
 44  LowQualFinSF         413 non-null    int64  
 45  GrLivArea            413 non-null    float64
 46  BsmtFullBath         413 non-null    int64  
 47  BsmtHalfBath         413 non-null    int64  
 48  FullBath             413 non-null    int64  
 49  HalfBath             413 non-null    int64  
 50  BedroomAbvGr         413 non-null    int64  
 51  KitchenAbvGr         413 non-null    int64  
 52  KitchenQual          413 non-null    object 
 53  TotRmsAbvGrd         413 non-null    float64
 54  Functional           413 non-null    object 
 55  Fireplaces           413 non-null    int64  
 56  FireplaceQu          413 non-null    object 
 57  GarageType           413 non-null    object 
 58  GarageYrBlt          413 non-null    float64
 59  GarageFinish         413 non-null    object 
 60  GarageCars           413 non-null    int64  
 61  GarageArea           413 non-null    float64
 62  GarageQual           413 non-null    object 
 63  GarageCond           413 non-null    object 
 64  PavedDrive           413 non-null    object 
 65  WoodDeckSF           413 non-null    float64
 66  OpenPorchSF          413 non-null    float64
 67  EnclosedPorch        413 non-null    int64  
 68  ThrSsnPorch          413 non-null    int64  
 69  ScreenPorch          413 non-null    int64  
 70  PoolArea             413 non-null    int64  
 71  PoolQC               413 non-null    object 
 72  Fence                413 non-null    object 
 73  MiscFeature          413 non-null    object 
 74  MiscVal              413 non-null    int64  
 75  MoSold               413 non-null    int64  
 76  YrSold               413 non-null    int64  
 77  SaleType             413 non-null    object 
 78  SaleCondition        413 non-null    object 
 79  Property_Sale_Price  413 non-null    float64
dtypes: float64(16), int64(21), object(43)
memory usage: 258.3+ KB


model_1 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LotFrontage + LotArea + Road_Type + Alley + Property_Shape + LandContour + Utilities + LotConfig + LandSlope + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + YearBuilt + YearRemodAdd + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + MasVnrArea + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinSF1 + BsmtFinType2 + BsmtFinSF2 + BsmtUnfSF + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + SecondFlrSF + LowQualFinSF + GrLivArea + BsmtFullBath + BsmtHalfBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + TotRmsAbvGrd + Functional + Fireplaces + FireplaceQu + GarageType + GarageYrBlt + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + WoodDeckSF + OpenPorchSF + EnclosedPorch + ThrSsnPorch + ScreenPorch + PoolArea + PoolQC + Fence + MiscFeature + MiscVal + MoSold + YrSold + SaleType + SaleCondition', data = trn).fit()
print(model_1.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.922
Model:                             OLS   Adj. R-squared:                  0.916
Method:                  Least Squares   F-statistic:                     169.5
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:38:53   Log-Likelihood:                -18690.
No. Observations:                 1660   AIC:                         3.760e+04
Df Residuals:                     1551   BIC:                         3.819e+04
Df Model:                          108                                         
Covariance Type:             nonrobust                                         
===============================================================================================
                                  coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------------------
Intercept                   -6.749e+05   7.95e+05     -0.849      0.396   -2.23e+06    8.84e+05
Zone_Class[T.FV]             2.963e+04   7727.545      3.835      0.000    1.45e+04    4.48e+04
Zone_Class[T.RL]             2.755e+04   7116.147      3.872      0.000    1.36e+04    4.15e+04
Zone_Class[T.RM]             2.454e+04   6921.022      3.545      0.000     1.1e+04    3.81e+04
Road_Type[T.Pave]            1.968e+04   8051.279      2.445      0.015    3890.304    3.55e+04
Alley[T.No alley access]     6485.3589   2862.152      2.266      0.024     871.263    1.21e+04
Property_Shape[T.Reg]        -889.2642   1153.934     -0.771      0.441   -3152.699    1374.171
LandContour[T.HLS]           4827.0355   3356.096      1.438      0.151   -1755.929    1.14e+04
LandContour[T.Lvl]           2637.4755   2592.292      1.017      0.309   -2447.291    7722.242
Utilities[T.NoSeWa]         -4.043e+04   2.04e+04     -1.984      0.047   -8.04e+04    -466.047
LotConfig[T.Inside]         -1295.0973   1156.392     -1.120      0.263   -3563.354     973.159
LandSlope[T.Mod]             5929.2495   2872.915      2.064      0.039     294.042    1.16e+04
LandSlope[T.Sev]            -3948.6265   6070.068     -0.651      0.515   -1.59e+04    7957.780
Neighborhood[T.Edwards]     -8897.2469   2231.996     -3.986      0.000   -1.33e+04   -4519.199
Neighborhood[T.IDOTRR]      -1.244e+04   3747.368     -3.321      0.001   -1.98e+04   -5094.071
Neighborhood[T.NAmes]       -6566.5026   1900.185     -3.456      0.001   -1.03e+04   -2839.300
Neighborhood[T.NridgHt]      2.544e+04   2412.632     10.544      0.000    2.07e+04    3.02e+04
Neighborhood[T.Somerst]      1.345e+04   2091.908      6.427      0.000    9342.206    1.75e+04
Neighborhood[T.StoneBr]      2.371e+04   3666.257      6.468      0.000    1.65e+04    3.09e+04
Condition1[T.Norm]           9301.3870   1790.766      5.194      0.000    5788.810    1.28e+04
Condition1[T.PosN]           9189.5809   4006.355      2.294      0.022    1331.138     1.7e+04
Condition2[T.PosN]          -3.775e+04   1.24e+04     -3.035      0.002   -6.22e+04   -1.34e+04
Dwelling_Type[T.TwnhsE]      7897.6068   2735.272      2.887      0.004    2532.386    1.33e+04
HouseStyle[T.2Story]        -4543.1375   2076.776     -2.188      0.029   -8616.722    -469.553
RoofStyle[T.Hip]              456.4936   1328.581      0.344      0.731   -2149.510    3062.497
RoofMatl[T.WdShngl]          1.471e+04   5977.673      2.461      0.014    2984.379    2.64e+04
Exterior1st[T.MetalSd]      -2931.8808   4284.591     -0.684      0.494   -1.13e+04    5472.322
Exterior1st[T.Plywood]       1461.1274   4595.671      0.318      0.751   -7553.257    1.05e+04
Exterior1st[T.VinylSd]      -5954.0446   5124.057     -1.162      0.245    -1.6e+04    4096.767
Exterior2nd[T.VinylSd]       8008.9386   2788.594      2.872      0.004    2539.126    1.35e+04
MasVnrType[T.None]           -562.5398   1822.101     -0.309      0.758   -4136.581    3011.501
MasVnrType[T.Stone]          3348.7692   2112.328      1.585      0.113    -794.552    7492.090
ExterQual[T.TA]             -7596.6591   1748.176     -4.345      0.000    -1.1e+04   -4167.621
ExterCond[T.TA]             -3203.3515   3884.308     -0.825      0.410   -1.08e+04    4415.699
Foundation[T.CBlock]          433.6483   2155.065      0.201      0.841   -3793.501    4660.797
Foundation[T.PConc]          2032.2332   2400.371      0.847      0.397   -2676.082    6740.549
BsmtQual[T.Gd]              -1.197e+04   2439.586     -4.908      0.000   -1.68e+04   -7187.693
BsmtQual[T.No Basement]     -1.003e+04   4467.750     -2.246      0.025   -1.88e+04   -1270.274
BsmtQual[T.TA]              -1.092e+04   2942.495     -3.712      0.000   -1.67e+04   -5151.988
BsmtCond[T.No basement]      -1.44e+04   3908.856     -3.685      0.000   -2.21e+04   -6735.141
BsmtCond[T.TA]              -3044.4932   2386.896     -1.276      0.202   -7726.377    1637.391
BsmtExposure[T.Gd]           7795.1322   2145.021      3.634      0.000    3587.685     1.2e+04
BsmtExposure[T.No]          -2906.7901   1299.539     -2.237      0.025   -5455.829    -357.751
BsmtFinType1[T.GLQ]          6687.7085   1594.395      4.195      0.000    3560.311    9815.106
BsmtFinType2[T.Rec]         -2.467e+04   6214.959     -3.969      0.000   -3.69e+04   -1.25e+04
BsmtFinType2[T.Unf]         -2.113e+04   5879.874     -3.593      0.000   -3.27e+04   -9592.136
Heating[T.Grav]             -5664.1501   5295.845     -1.070      0.285   -1.61e+04    4723.622
HeatingQC[T.TA]             -1724.7267   1312.946     -1.314      0.189   -4300.063     850.609
CentralAir[T.Y]              4793.3559   2622.360      1.828      0.068    -350.390    9937.102
Electrical[T.SBrkr]         -3546.3578   2024.558     -1.752      0.080   -7517.518     424.803
KitchenQual[T.Fa]           -2.001e+04   4418.394     -4.528      0.000   -2.87e+04   -1.13e+04
KitchenQual[T.Gd]           -1.742e+04   2473.706     -7.042      0.000   -2.23e+04   -1.26e+04
KitchenQual[T.TA]           -2.194e+04   2832.384     -7.747      0.000   -2.75e+04   -1.64e+04
Functional[T.Typ]            1.816e+04   2195.582      8.273      0.000    1.39e+04    2.25e+04
FireplaceQu[T.Fa]           -5996.3476   5376.885     -1.115      0.265   -1.65e+04    4550.383
FireplaceQu[T.Gd]             239.5142   4157.933      0.058      0.954   -7916.250    8395.279
FireplaceQu[T.No Fireplace]   755.0856   4727.501      0.160      0.873   -8517.882       1e+04
FireplaceQu[T.TA]            -336.1986   4289.009     -0.078      0.938   -8749.067    8076.669
GarageType[T.Detchd]         -937.6517   1545.568     -0.607      0.544   -3969.275    2093.972
GarageFinish[T.No Garage]    2.286e+04   4857.703      4.706      0.000    1.33e+04    3.24e+04
GarageFinish[T.RFn]         -2779.9769   1242.658     -2.237      0.025   -5217.443    -342.510
GarageQual[T.No Garage]     -2.363e+04   6344.235     -3.725      0.000   -3.61e+04   -1.12e+04
GarageQual[T.TA]            -1.541e+04   5325.951     -2.894      0.004   -2.59e+04   -4964.888
GarageCond[T.TA]             4020.9634   3752.310      1.072      0.284   -3339.172    1.14e+04
PavedDrive[T.Y]              1854.0195   2128.706      0.871      0.384   -2321.426    6029.465
PoolQC[T.Fa]                -1.888e+04   2.16e+04     -0.873      0.383   -6.13e+04    2.35e+04
PoolQC[T.Gd]                -5.973e+04   2.02e+04     -2.951      0.003   -9.94e+04      -2e+04
PoolQC[T.No Pool]            3.982e+05   4.96e+04      8.032      0.000    3.01e+05    4.95e+05
Fence[T.No Fence]             -89.7155   1488.254     -0.060      0.952   -3008.918    2829.487
MiscFeature[T.Shed]          4627.9797   2880.953      1.606      0.108   -1022.995    1.03e+04
SaleType[T.New]              2.263e+04   6414.540      3.527      0.000       1e+04    3.52e+04
SaleCondition[T.Normal]      9818.3825   1829.095      5.368      0.000    6230.623    1.34e+04
SaleCondition[T.Partial]    -8682.4420   6614.107     -1.313      0.189   -2.17e+04    4291.093
Dwell_Type                    -74.4296     24.175     -3.079      0.002    -121.848     -27.011
LotFrontage                    17.5421     34.166      0.513      0.608     -49.474      84.558
LotArea                         0.5284      0.180      2.942      0.003       0.176       0.881
OverallQual                  3437.6671    617.771      5.565      0.000    2225.912    4649.422
OverallCond                  2882.9417    966.907      2.982      0.003     986.358    4779.526
YearBuilt                      18.1211     35.294      0.513      0.608     -51.108      87.350
YearRemodAdd                  217.8828     38.083      5.721      0.000     143.182     292.583
MasVnrArea                      5.2840      6.626      0.798      0.425      -7.712      18.280
BsmtFinSF1                    -34.9881      8.868     -3.946      0.000     -52.382     -17.594
BsmtFinSF2                    -37.7913      9.405     -4.018      0.000     -56.239     -19.343
BsmtUnfSF                     -44.9115      8.998     -4.991      0.000     -62.562     -27.261
TotalBsmtSF                    67.6645      9.811      6.897      0.000      48.421      86.908
FirstFlrSF                    -20.2990      8.392     -2.419      0.016     -36.760      -3.838
SecondFlrSF                    -9.4071      7.545     -1.247      0.213     -24.206       5.392
LowQualFinSF                  -35.7420     12.506     -2.858      0.004     -60.272     -11.212
GrLivArea                      59.7318      7.970      7.495      0.000      44.099      75.365
BsmtFullBath                -1285.8205    741.784     -1.733      0.083   -2740.826     169.185
BsmtHalfBath                 -744.4021   2273.542     -0.327      0.743   -5203.943    3715.139
FullBath                     2557.2845   1611.948      1.586      0.113    -604.543    5719.112
HalfBath                     1797.2025   1544.836      1.163      0.245   -1232.986    4827.391
BedroomAbvGr                 -242.7745   1055.018     -0.230      0.818   -2312.186    1826.637
KitchenAbvGr                -1.929e+04   3769.829     -5.118      0.000   -2.67e+04   -1.19e+04
TotRmsAbvGrd                  836.2509    673.344      1.242      0.214    -484.509    2157.011
Fireplaces                   5986.1678   1885.715      3.174      0.002    2287.348    9684.988
GarageYrBlt                  -123.4127     42.658     -2.893      0.004    -207.085     -39.740
GarageCars                   3962.5207   1771.991      2.236      0.025     486.769    7438.272
GarageArea                     30.5763      6.126      4.992      0.000      18.561      42.592
WoodDeckSF                     16.5559      4.808      3.444      0.001       7.125      25.986
OpenPorchSF                    22.7239     11.116      2.044      0.041       0.919      44.529
EnclosedPorch                   5.5731      9.304      0.599      0.549     -12.677      23.823
ThrSsnPorch                    29.0589     17.311      1.679      0.093      -4.897      63.015
ScreenPorch                    30.9026      9.514      3.248      0.001      12.241      49.564
PoolArea                      699.3293     87.372      8.004      0.000     527.949     870.709
MiscVal                         1.2893      1.089      1.184      0.237      -0.847       3.426
MoSold                        179.2526    186.067      0.963      0.336    -185.716     544.221
YrSold                         28.1100    393.102      0.072      0.943    -742.958     799.178
==============================================================================
Omnibus:                      157.247   Durbin-Watson:                   2.037
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              956.841
Skew:                          -0.184   Prob(JB):                    1.68e-208
Kurtosis:                       6.701   Cond. No.                     1.90e+07
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.9e+07. This might indicate that there are
strong multicollinearity or other numerical problems.


model_2 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + Road_Type + Alley + Property_Shape + LandContour + LotConfig + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + RoofStyle + RoofMatl + Exterior1st + Exterior2nd + MasVnrType + ExterQual + ExterCond + Foundation + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + BsmtFinType2 + TotalBsmtSF + Heating + HeatingQC + CentralAir + Electrical + FirstFlrSF + GrLivArea + BsmtFullBath + FullBath + HalfBath + BedroomAbvGr + KitchenAbvGr + KitchenQual + Functional + Fireplaces + FireplaceQu + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + GarageCond + PavedDrive + Fence + MiscFeature + SaleType + SaleCondition', data = trn).fit()
print(model2.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.915
Model:                             OLS   Adj. R-squared:                  0.911
Method:                  Least Squares   F-statistic:                     222.9
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:38:54   Log-Likelihood:                -23428.
No. Observations:                 2073   AIC:                         4.705e+04
Df Residuals:                     1977   BIC:                         4.759e+04
Df Model:                           95                                         
Covariance Type:             nonrobust                                         
===============================================================================================
                                  coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------------------
Intercept                    2.949e+04   2.73e+04      1.081      0.280    -2.4e+04     8.3e+04
Zone_Class[T.FV]             3.107e+04   7231.259      4.297      0.000    1.69e+04    4.53e+04
Zone_Class[T.RL]              3.11e+04   6714.572      4.632      0.000    1.79e+04    4.43e+04
Zone_Class[T.RM]             2.663e+04   6525.447      4.080      0.000    1.38e+04    3.94e+04
Road_Type[T.Pave]             1.35e+04   8354.476      1.616      0.106   -2880.769    2.99e+04
Alley[T.No alley access]     3874.4335   2689.289      1.441      0.150   -1399.704    9148.571
Property_Shape[T.Reg]       -1375.5514   1063.990     -1.293      0.196   -3462.211     711.108
LandContour[T.HLS]           8915.2545   3073.450      2.901      0.004    2887.714    1.49e+04
LandContour[T.Lvl]           4916.7337   2411.037      2.039      0.042     188.293    9645.175
LotConfig[T.Inside]         -1480.8200   1057.304     -1.401      0.162   -3554.367     592.727
Neighborhood[T.Edwards]     -8284.9469   2013.304     -4.115      0.000   -1.22e+04   -4336.526
Neighborhood[T.IDOTRR]      -1.371e+04   3458.817     -3.965      0.000   -2.05e+04   -6930.046
Neighborhood[T.NAmes]       -6008.2102   1709.653     -3.514      0.000   -9361.121   -2655.299
Neighborhood[T.NridgHt]      2.789e+04   2169.062     12.860      0.000    2.36e+04    3.21e+04
Neighborhood[T.Somerst]      1.499e+04   1880.285      7.974      0.000    1.13e+04    1.87e+04
Neighborhood[T.StoneBr]      2.644e+04   3369.340      7.848      0.000    1.98e+04    3.31e+04
Condition1[T.Norm]           1.064e+04   1681.972      6.325      0.000    7340.458    1.39e+04
Condition1[T.PosN]           1.492e+04   3540.865      4.212      0.000    7970.970    2.19e+04
Condition2[T.Norm]            -49.4815   5460.027     -0.009      0.993   -1.08e+04    1.07e+04
Condition2[T.PosN]          -4.295e+04   1.35e+04     -3.180      0.001   -6.94e+04   -1.65e+04
Dwelling_Type[T.TwnhsE]      1.024e+04   2553.144      4.012      0.000    5236.497    1.53e+04
HouseStyle[T.1Story]          619.3548   1873.886      0.331      0.741   -3055.643    4294.353
HouseStyle[T.2Story]        -3780.8137   1917.995     -1.971      0.049   -7542.318     -19.309
RoofStyle[T.Hip]             1690.4723   1226.274      1.379      0.168    -714.453    4095.398
RoofMatl[T.WdShngl]          1.042e+04   5474.374      1.904      0.057    -312.074    2.12e+04
Exterior1st[T.MetalSd]      -8235.5834   5535.797     -1.488      0.137   -1.91e+04    2621.026
Exterior1st[T.Plywood]      -1159.7488   5527.049     -0.210      0.834    -1.2e+04    9679.705
Exterior1st[T.VinylSd]      -8741.0645   5810.747     -1.504      0.133   -2.01e+04    2654.767
Exterior2nd[T.HdBoard]       3443.8812   4219.797      0.816      0.415   -4831.835    1.17e+04
Exterior2nd[T.MetalSd]       8510.7359   4316.030      1.972      0.049      46.290     1.7e+04
Exterior2nd[T.VinylSd]       1.087e+04   4550.212      2.389      0.017    1944.769    1.98e+04
MasVnrType[T.None]          -1274.2169   1183.501     -1.077      0.282   -3595.257    1046.823
MasVnrType[T.Stone]          3305.9509   1947.921      1.697      0.090    -514.242    7126.144
ExterQual[T.Fa]             -3828.9733   7220.285     -0.530      0.596    -1.8e+04    1.03e+04
ExterQual[T.Gd]             -4239.2877   3141.431     -1.349      0.177   -1.04e+04    1921.577
ExterQual[T.TA]             -1.089e+04   3457.092     -3.149      0.002   -1.77e+04   -4107.815
ExterCond[T.TA]               238.0023   3845.841      0.062      0.951   -7304.326    7780.330
Foundation[T.CBlock]         2437.3976   1976.318      1.233      0.218   -1438.487    6313.282
Foundation[T.PConc]          1020.8542   2194.331      0.465      0.642   -3282.591    5324.299
BsmtQual[T.Gd]              -1.285e+04   2265.686     -5.672      0.000   -1.73e+04   -8408.576
BsmtQual[T.No Basement]     -1.389e+04   4103.430     -3.386      0.001   -2.19e+04   -5845.956
BsmtQual[T.TA]              -1.432e+04   2692.971     -5.319      0.000   -1.96e+04   -9043.355
BsmtCond[T.No basement]     -1.469e+04   3564.657     -4.121      0.000   -2.17e+04   -7697.860
BsmtCond[T.TA]              -5513.2068   2137.403     -2.579      0.010   -9705.006   -1321.407
BsmtExposure[T.Gd]           9462.7377   1933.198      4.895      0.000    5671.417    1.33e+04
BsmtExposure[T.No]          -4748.1988   1174.409     -4.043      0.000   -7051.408   -2444.990
BsmtExposure[T.No Basement] -9863.3394   1.44e+04     -0.686      0.493    -3.8e+04    1.83e+04
BsmtFinType1[T.GLQ]          9513.3563   1668.536      5.702      0.000    6241.083    1.28e+04
BsmtFinType1[T.No basement]   1.91e+04   2.56e+04      0.746      0.456   -3.11e+04    6.93e+04
BsmtFinType1[T.Unf]           857.2812   1287.577      0.666      0.506   -1667.870    3382.432
BsmtFinType2[T.Rec]         -1.092e+04   2.04e+04     -0.537      0.592   -5.08e+04     2.9e+04
BsmtFinType2[T.Unf]         -7507.2070   2.03e+04     -0.370      0.711   -4.73e+04    3.23e+04
Heating[T.Grav]             -2326.7430   5322.299     -0.437      0.662   -1.28e+04    8111.161
HeatingQC[T.Fa]             -5947.4515   3109.931     -1.912      0.056    -1.2e+04     151.635
HeatingQC[T.Gd]             -1932.1179   1437.285     -1.344      0.179   -4750.870     886.634
HeatingQC[T.TA]             -3562.4604   1397.771     -2.549      0.011   -6303.720    -821.201
CentralAir[T.Y]              3923.9751   2401.788      1.634      0.102    -786.326    8634.276
Electrical[T.SBrkr]           588.2151   1876.008      0.314      0.754   -3090.945    4267.375
KitchenQual[T.Fa]           -2.619e+04   4234.564     -6.185      0.000   -3.45e+04   -1.79e+04
KitchenQual[T.Gd]           -1.773e+04   2389.152     -7.423      0.000   -2.24e+04    -1.3e+04
KitchenQual[T.TA]           -2.512e+04   2642.193     -9.507      0.000   -3.03e+04   -1.99e+04
Functional[T.Typ]            1.663e+04   2042.323      8.142      0.000    1.26e+04    2.06e+04
FireplaceQu[T.Fa]           -2555.9064   5074.945     -0.504      0.615   -1.25e+04    7396.897
FireplaceQu[T.Gd]            1823.0485   3874.968      0.470      0.638   -5776.402    9422.499
FireplaceQu[T.No Fireplace] -1014.8925   4399.602     -0.231      0.818   -9643.237    7613.452
FireplaceQu[T.TA]            1147.8511   3991.360      0.288      0.774   -6679.864    8975.566
GarageType[T.BuiltIn]       -2130.6148   2225.456     -0.957      0.338   -6495.101    2233.871
GarageType[T.Detchd]        -4455.8961   1470.281     -3.031      0.002   -7339.360   -1572.432
GarageType[T.No Garage]     -2759.4344   6761.041     -0.408      0.683    -1.6e+04    1.05e+04
GarageFinish[T.No Garage]    1.732e+04   7645.305      2.265      0.024    2325.558    3.23e+04
GarageFinish[T.RFn]         -3942.0785   1325.972     -2.973      0.003   -6542.527   -1341.630
GarageFinish[T.Unf]         -1393.2860   1668.825     -0.835      0.404   -4666.127    1879.554
GarageQual[T.No Garage]     -2.498e+04   5863.492     -4.261      0.000   -3.65e+04   -1.35e+04
GarageQual[T.TA]             -1.95e+04   4999.433     -3.901      0.000   -2.93e+04   -9696.464
GarageCond[T.TA]             4465.7450   3468.501      1.288      0.198   -2336.557    1.13e+04
PavedDrive[T.Y]              -881.8291   1995.314     -0.442      0.659   -4794.969    3031.311
Fence[T.No Fence]           -1201.9158   1384.764     -0.868      0.386   -3917.666    1513.834
MiscFeature[T.Shed]          4254.3337   2529.973      1.682      0.093    -707.359    9216.027
SaleType[T.New]              2.814e+04   6361.898      4.424      0.000    1.57e+04    4.06e+04
SaleType[T.WD]               1101.1544   2338.566      0.471      0.638   -3485.158    5687.467
SaleCondition[T.Normal]      9558.9590   1739.497      5.495      0.000    6147.519     1.3e+04
SaleCondition[T.Partial]    -1.499e+04   6192.042     -2.420      0.016   -2.71e+04   -2842.286
Dwell_Type                   -117.1537     21.813     -5.371      0.000    -159.932     -74.375
OverallQual                  1368.3789    496.475      2.756      0.006     394.709    2342.048
OverallCond                  2952.1808    833.812      3.541      0.000    1316.939    4587.422
TotalBsmtSF                    23.0983      2.929      7.887      0.000      17.354      28.842
FirstFlrSF                     -8.1891      4.149     -1.974      0.049     -16.326      -0.052
GrLivArea                      53.0754      2.807     18.907      0.000      47.570      58.581
BsmtFullBath                -3036.6573    557.987     -5.442      0.000   -4130.961   -1942.353
FullBath                     2717.4349   1476.396      1.841      0.066    -178.020    5612.890
HalfBath                     3908.6039   1417.090      2.758      0.006    1129.458    6687.750
BedroomAbvGr                  503.5063    926.248      0.544      0.587   -1313.018    2320.030
KitchenAbvGr                -1.662e+04   3410.508     -4.875      0.000   -2.33e+04   -9936.000
Fireplaces                   4498.6900   1744.577      2.579      0.010    1077.287    7920.093
GarageCars                   4183.3804   1600.502      2.614      0.009    1044.533    7322.227
GarageArea                     28.4207      5.282      5.381      0.000      18.062      38.780
==============================================================================
Omnibus:                      330.370   Durbin-Watson:                   2.036
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2963.683
Skew:                          -0.465   Prob(JB):                         0.00
Kurtosis:                       8.783   Cond. No.                     2.23e+05
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 2.23e+05. This might indicate that there are
strong multicollinearity or other numerical problems.


trn["Condition2"].value_counts()

Feedr    1657
PosN        3
Name: Condition2, dtype: int64


trn["Condition2"].replace('Norm', 'Feedr', inplace = True)


trn["Condition2"].value_counts()

Feedr    1657
PosN        3
Name: Condition2, dtype: int64


trn["HouseStyle"].value_counts()

1.5Fin    1147
2Story     513
Name: HouseStyle, dtype: int64


trn["HouseStyle"].replace('1Story', '1.5Fin', inplace = True)


trn["HouseStyle"].value_counts()

1.5Fin    1147
2Story     513
Name: HouseStyle, dtype: int64


trn["Exterior2nd"].value_counts()

AsbShng    975
VinylSd    685
Name: Exterior2nd, dtype: int64


trn["Exterior2nd"].replace('HdBoard', 'AsbShng', inplace = True)
trn["Exterior2nd"].replace('MetalSd', 'AsbShng', inplace = True)


trn["Exterior2nd"].value_counts()

AsbShng    975
VinylSd    685
Name: Exterior2nd, dtype: int64


trn["ExterQual"].value_counts()

TA    1029
Ex     631
Name: ExterQual, dtype: int64


trn["ExterQual"].replace('Gd', 'Ex', inplace = True)
trn["ExterQual"].replace('Fa', 'Ex', inplace = True)


trn["ExterQual"].value_counts()

TA    1029
Ex     631
Name: ExterQual, dtype: int64


trn["BsmtExposure"].value_counts()

No    1095
Av     421
Gd     144
Name: BsmtExposure, dtype: int64


trn["BsmtExposure"].replace('No Basement', 'Av', inplace = True)


trn["BsmtExposure"].value_counts()

No    1095
Av     421
Gd     144
Name: BsmtExposure, dtype: int64


trn["BsmtFinType1"].value_counts()

BLQ    1188
GLQ     472
Name: BsmtFinType1, dtype: int64


trn["BsmtFinType1"].replace('Unf', 'BLQ', inplace = True)
trn["BsmtFinType1"].replace('No basement', 'BLQ', inplace = True)


trn["BsmtFinType1"].value_counts()

BLQ    1188
GLQ     472
Name: BsmtFinType1, dtype: int64


trn["HeatingQC"].value_counts()

Ex    1172
TA     488
Name: HeatingQC, dtype: int64


trn["HeatingQC"].replace('Gd', 'Ex', inplace = True)
trn["HeatingQC"].replace('Fa', 'Ex', inplace = True)


trn["HeatingQC"].value_counts()

Ex    1172
TA     488
Name: HeatingQC, dtype: int64


trn["GarageType"].value_counts()

Attchd    1195
Detchd     465
Name: GarageType, dtype: int64


trn["GarageType"].replace('No Garage', 'Attchd', inplace = True)
trn["GarageType"].replace('BuiltIn', 'Attchd', inplace = True)


trn["GarageType"].value_counts()

Attchd    1195
Detchd     465
Name: GarageType, dtype: int64


trn["GarageFinish"].value_counts()

Fin          1089
RFn           479
No Garage      92
Name: GarageFinish, dtype: int64


trn["GarageFinish"].replace('Unf', 'Fin', inplace = True)


trn["GarageFinish"].value_counts()

Fin          1089
RFn           479
No Garage      92
Name: GarageFinish, dtype: int64


trn["SaleType"].value_counts()

COD    1517
New     143
Name: SaleType, dtype: int64


trn["SaleType"].replace('WD', 'COD', inplace = True)


trn["SaleType"].value_counts()

COD    1517
New     143
Name: SaleType, dtype: int64


trn["BsmtCond"].value_counts()

TA             1491
No basement      93
Gd               76
Name: BsmtCond, dtype: int64


#trn["BsmtCond"].replace('TA', 'Gd', inplace = True)


trn["BsmtCond"].value_counts()

TA             1491
No basement      93
Gd               76
Name: BsmtCond, dtype: int64


trn["SaleCondition"].value_counts()

Normal     1375
Abnorml     144
Partial     141
Name: SaleCondition, dtype: int64


trn["SaleCondition"].replace('Partial', 'Abnorml', inplace = True)


trn["SaleCondition"].value_counts()

Normal     1375
Abnorml     285
Name: SaleCondition, dtype: int64


model_3 = smf.ols('Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition', data = trn).fit()
print(model_3.summary())

                             OLS Regression Results                            
===============================================================================
Dep. Variable:     Property_Sale_Price   R-squared:                       0.908
Model:                             OLS   Adj. R-squared:                  0.905
Method:                  Least Squares   F-statistic:                     330.3
Date:                 Mon, 27 Dec 2021   Prob (F-statistic):               0.00
Time:                         02:38:55   Log-Likelihood:                -18828.
No. Observations:                 1660   AIC:                         3.775e+04
Df Residuals:                     1611   BIC:                         3.802e+04
Df Model:                           48                                         
Covariance Type:             nonrobust                                         
=============================================================================================
                                coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------------
Intercept                  1.583e+04   1.42e+04      1.114      0.265    -1.2e+04    4.37e+04
Zone_Class[T.FV]           3.253e+04   7838.462      4.150      0.000    1.72e+04    4.79e+04
Zone_Class[T.RL]           3.062e+04   7236.072      4.231      0.000    1.64e+04    4.48e+04
Zone_Class[T.RM]           2.525e+04   7045.921      3.584      0.000    1.14e+04    3.91e+04
LandContour[T.HLS]         1.373e+04   3356.073      4.092      0.000    7149.616    2.03e+04
LandContour[T.Lvl]         7559.6763   2509.513      3.012      0.003    2637.422    1.25e+04
Neighborhood[T.Edwards]   -9256.6009   2180.037     -4.246      0.000   -1.35e+04   -4980.595
Neighborhood[T.IDOTRR]    -1.536e+04   3756.059     -4.090      0.000   -2.27e+04   -7994.650
Neighborhood[T.NAmes]     -6207.5088   1896.264     -3.274      0.001   -9926.912   -2488.106
Neighborhood[T.NridgHt]    2.795e+04   2412.130     11.588      0.000    2.32e+04    3.27e+04
Neighborhood[T.Somerst]    1.463e+04   2119.754      6.901      0.000    1.05e+04    1.88e+04
Neighborhood[T.StoneBr]    2.678e+04   3747.789      7.147      0.000    1.94e+04    3.41e+04
Condition1[T.Norm]          1.07e+04   1843.463      5.805      0.000    7084.618    1.43e+04
Condition1[T.PosN]         1.234e+04   4047.992      3.049      0.002    4404.346    2.03e+04
Condition2[T.PosN]        -3.955e+04   1.27e+04     -3.126      0.002   -6.44e+04   -1.47e+04
Dwelling_Type[T.TwnhsE]    9707.7886   2654.211      3.658      0.000    4501.720    1.49e+04
HouseStyle[T.2Story]      -4782.5391   1801.143     -2.655      0.008   -8315.369   -1249.709
Exterior2nd[T.VinylSd]     6011.0864   1322.490      4.545      0.000    3417.105    8605.068
ExterQual[T.TA]           -7012.3820   1756.923     -3.991      0.000   -1.05e+04   -3566.287
BsmtQual[T.Gd]            -1.491e+04   2422.938     -6.156      0.000   -1.97e+04   -1.02e+04
BsmtQual[T.No Basement]   -1.102e+04   4234.930     -2.602      0.009   -1.93e+04   -2711.982
BsmtQual[T.TA]            -1.631e+04   2866.936     -5.691      0.000   -2.19e+04   -1.07e+04
BsmtCond[T.No basement]   -1.236e+04   3801.225     -3.251      0.001   -1.98e+04   -4902.647
BsmtCond[T.TA]            -3850.0053   2477.860     -1.554      0.120   -8710.174    1010.163
BsmtExposure[T.Gd]         8561.0111   2188.061      3.913      0.000    4269.266    1.29e+04
BsmtExposure[T.No]        -5002.6197   1308.817     -3.822      0.000   -7569.783   -2435.456
BsmtFinType1[T.GLQ]        8453.1112   1524.050      5.546      0.000    5463.782    1.14e+04
KitchenQual[T.Fa]          -2.79e+04   4395.959     -6.346      0.000   -3.65e+04   -1.93e+04
KitchenQual[T.Gd]         -1.779e+04   2554.751     -6.965      0.000   -2.28e+04   -1.28e+04
KitchenQual[T.TA]          -2.58e+04   2876.050     -8.971      0.000   -3.14e+04   -2.02e+04
Functional[T.Typ]          1.826e+04   2206.642      8.274      0.000    1.39e+04    2.26e+04
GarageType[T.Detchd]      -4602.4055   1519.836     -3.028      0.002   -7583.468   -1621.343
GarageFinish[T.No Garage]  1.621e+04   4081.455      3.972      0.000    8204.001    2.42e+04
GarageFinish[T.RFn]       -2798.5323   1275.744     -2.194      0.028   -5300.825    -296.240
GarageQual[T.No Garage]   -2.946e+04   5969.936     -4.934      0.000   -4.12e+04   -1.77e+04
GarageQual[T.TA]          -1.871e+04   5243.071     -3.568      0.000    -2.9e+04   -8422.456
SaleType[T.New]            1.494e+04   2665.241      5.605      0.000    9711.565    2.02e+04
SaleCondition[T.Normal]     1.14e+04   1834.404      6.213      0.000    7798.793     1.5e+04
Dwell_Type                 -105.7768     23.104     -4.578      0.000    -151.094     -60.459
OverallQual                2788.0056    635.391      4.388      0.000    1541.726    4034.285
OverallCond                4456.5227    955.884      4.662      0.000    2581.616    6331.429
TotalBsmtSF                  17.8734      2.343      7.630      0.000      13.279      22.468
GrLivArea                    53.7170      1.983     27.087      0.000      49.827      57.607
BsmtFullBath              -2877.0103    628.190     -4.580      0.000   -4109.167   -1644.854
HalfBath                   3420.1728   1463.072      2.338      0.020     550.448    6289.898
KitchenAbvGr               -1.53e+04   3700.618     -4.135      0.000   -2.26e+04   -8043.051
Fireplaces                 6134.1023    998.570      6.143      0.000    4175.469    8092.736
GarageCars                 6587.3430   1802.320      3.655      0.000    3052.204    1.01e+04
GarageArea                   20.9521      6.042      3.468      0.001       9.101      32.804
==============================================================================
Omnibus:                      271.617   Durbin-Watson:                   2.013
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2253.920
Skew:                          -0.509   Prob(JB):                         0.00
Kurtosis:                       8.617   Cond. No.                     6.49e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 6.49e+04. This might indicate that there are
strong multicollinearity or other numerical problems.


trn.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1660 entries, 0 to 1659
Data columns (total 80 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           1660 non-null   int64  
 1   Zone_Class           1660 non-null   object 
 2   LotFrontage          1660 non-null   float64
 3   LotArea              1660 non-null   float64
 4   Road_Type            1660 non-null   object 
 5   Alley                1660 non-null   object 
 6   Property_Shape       1660 non-null   object 
 7   LandContour          1660 non-null   object 
 8   Utilities            1660 non-null   object 
 9   LotConfig            1660 non-null   object 
 10  LandSlope            1660 non-null   object 
 11  Neighborhood         1660 non-null   object 
 12  Condition1           1660 non-null   object 
 13  Condition2           1660 non-null   object 
 14  Dwelling_Type        1660 non-null   object 
 15  HouseStyle           1660 non-null   object 
 16  OverallQual          1660 non-null   int64  
 17  OverallCond          1660 non-null   int64  
 18  YearBuilt            1660 non-null   float64
 19  YearRemodAdd         1660 non-null   int64  
 20  RoofStyle            1660 non-null   object 
 21  RoofMatl             1660 non-null   object 
 22  Exterior1st          1660 non-null   object 
 23  Exterior2nd          1660 non-null   object 
 24  MasVnrType           1660 non-null   object 
 25  MasVnrArea           1660 non-null   float64
 26  ExterQual            1660 non-null   object 
 27  ExterCond            1660 non-null   object 
 28  Foundation           1660 non-null   object 
 29  BsmtQual             1660 non-null   object 
 30  BsmtCond             1660 non-null   object 
 31  BsmtExposure         1660 non-null   object 
 32  BsmtFinType1         1660 non-null   object 
 33  BsmtFinSF1           1660 non-null   float64
 34  BsmtFinType2         1660 non-null   object 
 35  BsmtFinSF2           1660 non-null   int64  
 36  BsmtUnfSF            1660 non-null   float64
 37  TotalBsmtSF          1660 non-null   float64
 38  Heating              1660 non-null   object 
 39  HeatingQC            1660 non-null   object 
 40  CentralAir           1660 non-null   object 
 41  Electrical           1660 non-null   object 
 42  FirstFlrSF           1660 non-null   float64
 43  SecondFlrSF          1660 non-null   float64
 44  LowQualFinSF         1660 non-null   int64  
 45  GrLivArea            1660 non-null   float64
 46  BsmtFullBath         1660 non-null   int64  
 47  BsmtHalfBath         1660 non-null   int64  
 48  FullBath             1660 non-null   int64  
 49  HalfBath             1660 non-null   int64  
 50  BedroomAbvGr         1660 non-null   int64  
 51  KitchenAbvGr         1660 non-null   int64  
 52  KitchenQual          1660 non-null   object 
 53  TotRmsAbvGrd         1660 non-null   float64
 54  Functional           1660 non-null   object 
 55  Fireplaces           1660 non-null   int64  
 56  FireplaceQu          1660 non-null   object 
 57  GarageType           1660 non-null   object 
 58  GarageYrBlt          1660 non-null   float64
 59  GarageFinish         1660 non-null   object 
 60  GarageCars           1660 non-null   int64  
 61  GarageArea           1660 non-null   float64
 62  GarageQual           1660 non-null   object 
 63  GarageCond           1660 non-null   object 
 64  PavedDrive           1660 non-null   object 
 65  WoodDeckSF           1660 non-null   float64
 66  OpenPorchSF          1660 non-null   float64
 67  EnclosedPorch        1660 non-null   int64  
 68  ThrSsnPorch          1660 non-null   int64  
 69  ScreenPorch          1660 non-null   int64  
 70  PoolArea             1660 non-null   int64  
 71  PoolQC               1660 non-null   object 
 72  Fence                1660 non-null   object 
 73  MiscFeature          1660 non-null   object 
 74  MiscVal              1660 non-null   int64  
 75  MoSold               1660 non-null   int64  
 76  YrSold               1660 non-null   int64  
 77  SaleType             1660 non-null   object 
 78  SaleCondition        1660 non-null   object 
 79  Property_Sale_Price  1660 non-null   float64
dtypes: float64(16), int64(21), object(43)
memory usage: 1.0+ MB


trn1 = trn[['Dwell_Type', 'Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2', 'Dwelling_Type',
                   'HouseStyle', 'OverallQual', 'OverallCond', 'Exterior2nd', 'ExterQual', 'BsmtQual', 'BsmtCond',
                   'BsmtExposure', 'BsmtFinType1', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
                   'KitchenQual', 'Functional', 'Fireplaces', 'GarageType', 'GarageFinish', 'GarageCars', 'GarageArea',
                   'GarageQual', 'SaleType', 'SaleCondition', 'Property_Sale_Price']]
trn1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1660 entries, 0 to 1659
Data columns (total 32 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           1660 non-null   int64  
 1   Zone_Class           1660 non-null   object 
 2   LandContour          1660 non-null   object 
 3   Neighborhood         1660 non-null   object 
 4   Condition1           1660 non-null   object 
 5   Condition2           1660 non-null   object 
 6   Dwelling_Type        1660 non-null   object 
 7   HouseStyle           1660 non-null   object 
 8   OverallQual          1660 non-null   int64  
 9   OverallCond          1660 non-null   int64  
 10  Exterior2nd          1660 non-null   object 
 11  ExterQual            1660 non-null   object 
 12  BsmtQual             1660 non-null   object 
 13  BsmtCond             1660 non-null   object 
 14  BsmtExposure         1660 non-null   object 
 15  BsmtFinType1         1660 non-null   object 
 16  TotalBsmtSF          1660 non-null   float64
 17  GrLivArea            1660 non-null   float64
 18  BsmtFullBath         1660 non-null   int64  
 19  HalfBath             1660 non-null   int64  
 20  KitchenAbvGr         1660 non-null   int64  
 21  KitchenQual          1660 non-null   object 
 22  Functional           1660 non-null   object 
 23  Fireplaces           1660 non-null   int64  
 24  GarageType           1660 non-null   object 
 25  GarageFinish         1660 non-null   object 
 26  GarageCars           1660 non-null   int64  
 27  GarageArea           1660 non-null   float64
 28  GarageQual           1660 non-null   object 
 29  SaleType             1660 non-null   object 
 30  SaleCondition        1660 non-null   object 
 31  Property_Sale_Price  1660 non-null   float64
dtypes: float64(4), int64(8), object(20)
memory usage: 415.1+ KB


test1 = test[['Dwell_Type', 'Zone_Class', 'LandContour', 'Neighborhood', 'Condition1', 'Condition2', 'Dwelling_Type',
                   'HouseStyle', 'OverallQual', 'OverallCond', 'Exterior2nd', 'ExterQual', 'BsmtQual', 'BsmtCond',
                   'BsmtExposure', 'BsmtFinType1', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr',
                   'KitchenQual', 'Functional', 'Fireplaces', 'GarageType', 'GarageFinish', 'GarageCars', 'GarageArea',
                   'GarageQual', 'SaleType', 'SaleCondition', 'Property_Sale_Price']]
test1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 413 entries, 1660 to 2072
Data columns (total 32 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   Dwell_Type           413 non-null    int64  
 1   Zone_Class           413 non-null    object 
 2   LandContour          413 non-null    object 
 3   Neighborhood         413 non-null    object 
 4   Condition1           413 non-null    object 
 5   Condition2           413 non-null    object 
 6   Dwelling_Type        413 non-null    object 
 7   HouseStyle           413 non-null    object 
 8   OverallQual          413 non-null    int64  
 9   OverallCond          413 non-null    int64  
 10  Exterior2nd          413 non-null    object 
 11  ExterQual            413 non-null    object 
 12  BsmtQual             413 non-null    object 
 13  BsmtCond             413 non-null    object 
 14  BsmtExposure         413 non-null    object 
 15  BsmtFinType1         413 non-null    object 
 16  TotalBsmtSF          413 non-null    float64
 17  GrLivArea            413 non-null    float64
 18  BsmtFullBath         413 non-null    int64  
 19  HalfBath             413 non-null    int64  
 20  KitchenAbvGr         413 non-null    int64  
 21  KitchenQual          413 non-null    object 
 22  Functional           413 non-null    object 
 23  Fireplaces           413 non-null    int64  
 24  GarageType           413 non-null    object 
 25  GarageFinish         413 non-null    object 
 26  GarageCars           413 non-null    int64  
 27  GarageArea           413 non-null    float64
 28  GarageQual           413 non-null    object 
 29  SaleType             413 non-null    object 
 30  SaleCondition        413 non-null    object 
 31  Property_Sale_Price  413 non-null    float64
dtypes: float64(4), int64(8), object(20)
memory usage: 103.4+ KB


x_test = test1.loc[:, test1.columns != 'Property_Sale_Price']
y_test = test1.loc[:, test1.columns == 'Property_Sale_Price']


pred_1 = model_3.predict(x_test)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\lib\site-packages\patsy\categorical.py in categorical_to_int(data, levels, NA_action, origin)
    345             try:
--> 346                 out[i] = level_to_int[value]
    347             except KeyError:

KeyError: 'Partial'

During handling of the above exception, another exception occurred:

PatsyError                                Traceback (most recent call last)
~\anaconda3\lib\site-packages\statsmodels\base\model.py in predict(self, exog, transform, *args, **kwargs)
   1076             try:
-> 1077                 exog = dmatrix(design_info, exog, return_type="dataframe")
   1078             except Exception as exc:

~\anaconda3\lib\site-packages\patsy\highlevel.py in dmatrix(formula_like, data, eval_env, NA_action, return_type)
    289     eval_env = EvalEnvironment.capture(eval_env, reference=1)
--> 290     (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,
    291                                       NA_action, return_type)

~\anaconda3\lib\site-packages\patsy\highlevel.py in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type)
    166     if design_infos is not None:
--> 167         return build_design_matrices(design_infos, data,
    168                                      NA_action=NA_action,

~\anaconda3\lib\site-packages\patsy\build.py in build_design_matrices(design_infos, data, NA_action, return_type, dtype)
    887             if factor_info not in factor_info_to_values:
--> 888                 value, is_NA = _eval_factor(factor_info, data, NA_action)
    889                 factor_info_to_isNAs[factor_info] = is_NA

~\anaconda3\lib\site-packages\patsy\build.py in _eval_factor(factor_info, data, NA_action)
     83         assert factor_info.type == "categorical"
---> 84         result = categorical_to_int(result, factor_info.categories, NA_action,
     85                                     origin=factor_info.factor)

~\anaconda3\lib\site-packages\patsy\categorical.py in categorical_to_int(data, levels, NA_action, origin)
    358                 level_str = "[%s]" % (", ".join(level_strs))
--> 359                 raise PatsyError("Error converting data to categorical: "
    360                                  "observation with value %r does not match "

PatsyError: Error converting data to categorical: observation with value 'Partial' does not match any of the expected levels (expected: ['Abnorml', 'Normal'])
    Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition
                                                                                                                                                                                                                                                                                                                                                                                                                                           ^^^^^^^^^^^^^

During handling of the above exception, another exception occurred:

PatsyError                                Traceback (most recent call last)
<ipython-input-1350-12d4354fb999> in <module>
----> 1 pred_1 = model_3.predict(x_test)

~\anaconda3\lib\site-packages\statsmodels\base\model.py in predict(self, exog, transform, *args, **kwargs)
   1082                        '\n\nThe original error message returned by patsy is:\n'
   1083                        '{0}'.format(str(str(exc))))
-> 1084                 raise exc.__class__(msg)
   1085             if orig_exog_len > len(exog) and not is_dict:
   1086                 if exog_index is None:

PatsyError: predict requires that you use a DataFrame when predicting from a model
that was created using the formula api.

The original error message returned by patsy is:
Error converting data to categorical: observation with value 'Partial' does not match any of the expected levels (expected: ['Abnorml', 'Normal'])
    Property_Sale_Price ~ Dwell_Type + Zone_Class + LandContour + Neighborhood + Condition1 + Condition2 + Dwelling_Type + HouseStyle + OverallQual + OverallCond + Exterior2nd + ExterQual + BsmtQual + BsmtCond + BsmtExposure + BsmtFinType1 + TotalBsmtSF + GrLivArea + BsmtFullBath + HalfBath + KitchenAbvGr + KitchenQual + Functional + Fireplaces + GarageType + GarageFinish + GarageCars + GarageArea + GarageQual + SaleType + SaleCondition
                                                                                                                                                                                                                                                                                                                                                                                                                                           ^^^^^^^^^^^^^


y_test_series = y_test.iloc[:, 0]


resd_1 = y_test_series - pred_1


plt.figure(figsize=(12,10))
sns.distplot(trn1.resd_1, color='skyblue')
plt.title('Histogram of Residuals')
plt.ylabel('Prob')
plt.xlabel('Residuals')
plt.show()


trn1.info()


numerical_trn1 = trn1.select_dtypes(include=np.number)
print(len(numerical_trn1.columns))
numerical_trn1.columns


categorical_trn1 = trn1.select_dtypes(include = 'O')
print(len(categorical_trn1.columns))
categorical_trn1.columns


### With Good Predictors

a = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 
     'Fireplaces', 'GarageCars', 'GarageArea', 'Property_Sale_Price']


trn1_pp = trn1[a]


sns.pairplot(trn1_pp ,palette = sns.color_palette('Set3_r'), markers = ['>'], plot_kws = {'facecolors':'#00FF00'})


# visualize the relationship between the features and the response using scatterplots

sns.pairplot(trn1, x_vars = ['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 'KitchenAbvGr', 
     'Fireplaces', 'GarageCars', 'GarageArea'],
             y_vars = 'Property_Sale_Price', size=7, aspect=0.7)


# Now We Create Observation Number

obs = np.arange(1660) 
obs 

obsno = pd.DataFrame(obs) # convert array into a data frame 

trn1['obsno'] = obsno # craete a new variable/column 'obsno' in yr data


plt.figure(figsize=(12,10))
sns.scatterplot(x = trn1.obsno, y = trn1.resd_1, palette='spring_r', color='darkslateblue', markers='+')
plt.title('Scatterplot of obsno vs residuals')


plt.figure(figsize=(12,10))
sns.scatterplot(x = trn1.predict, y = trn1.resd_1, palette='spring', color='teal')
plt.title('Scatterplot of predict vs residuals')


trn1.info()


x = trn1[['Dwell_Type', 'OverallQual', 'OverallCond', 'TotalBsmtSF', 'GrLivArea', 'BsmtFullBath', 'HalfBath', 
            'KitchenAbvGr', 'Fireplaces', 'GarageCars', 'GarageArea']]


x.columns


vif_trn = pd.DataFrame()
vif_trn["feature"] = x.columns


# calculating VIF for each feature

from statsmodels.stats.outliers_influence import variance_inflation_factor

vif_trn["VIF"] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]
  
print(vif_trn)


# import numpy and statsmodels for durbin watson

import numpy as np
from statsmodels.stats.stattools import durbin_watson

gfg = durbin_watson(model_3.resid)


print("Durbin Watson Value :", round(gfg, 3))


se_1 = resd_1.pow(2)


sse_1 = se_1.sum()


msse_1 = sse_1/len(se_1)


import math
math.sqrt(msse_1)


obsno = np.arange(1660) 


fig, ax = plt.subplots()
ax.plot(obsno, y_test_series, 'o', label='Data')
ax.plot(obsno, pred_1, 'r-', label='Predicted')
ax.legend(loc='best')

	count	mean	std	min	25%	50%	75%	max
OverallQual
1	2.0	50150.000000	15344.217152	39300.0	44725.0	50150.0	55575.00	61000.0
2	4.0	47655.500000	14254.200796	35311.0	35311.0	47655.5	60000.00	60000.0
3	20.0	87473.750000	24688.567399	37900.0	75500.0	86250.0	97500.00	139600.0
4	214.0	136667.672897	58078.734600	34900.0	101850.0	124250.0	153750.00	340000.0
5	508.0	142626.137795	43095.950014	55993.0	119500.0	135250.0	154925.00	340000.0
6	463.0	166339.904968	43392.607283	75000.0	139750.0	163000.0	185000.00	340000.0
7	416.0	202321.163462	51486.310520	64500.0	172350.0	197000.0	230000.00	340000.0
8	265.0	237304.981132	69148.120764	64500.0	184100.0	245000.0	287000.00	340000.0
9	164.0	212890.189024	89622.432861	37900.0	137875.0	187500.0	316140.25	340000.0
10	17.0	319250.000000	55573.290572	160000.0	340000.0	340000.0	340000.00	340000.0

	count	mean	std	min	25%	50%	75%	max
BsmtFullBath
0	1210.0	164453.647934	59900.550784	34900.0	120625.0	155417.5	193969.75	340000.0
1	838.0	195526.536993	71769.877578	35311.0	140000.0	176750.0	240000.00	340000.0
2	23.0	215271.565217	82149.958655	108959.0	147950.0	188000.0	292000.00	340000.0
3	2.0	179000.000000	0.000000	179000.0	179000.0	179000.0	179000.00	179000.0

	Id	Dwell_Type	Zone_Class	LotFrontage	LotArea	Road_Type	Alley	Property_Shape	LandContour	Utilities	...	PoolQC	Fence	MiscFeature	MoSold	YrSold	SaleType	SaleCondition	Property_Sale_Price
1664	1665	50	RL	107.0	8758	Pave	NaN	Reg	Lvl	AllPub	...	NaN	GdWo	NaN	5	2007	WD	Normal	112000
1744	1745	60	RL	75.0	8168	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	7	2008	WD	Normal	222500
1607	1608	50	RL	103.0	6998	Pave	NaN	Reg	Low	AllPub	...	NaN	NaN	NaN	5	2006	WD	Normal	160000
136	137	20	RL	NaN	10355	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	7	2007	WD	Normal	143000
1928	974	20	FV	95.0	11639	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	12	2008	New	Partial	182000
1028	1029	50	RL	79.0	9492	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	4	2007	WD	Normal	105000
2046	920	20	RL	87.0	11029	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	5	2008	WD	Normal	176500
1119	1120	20	RL	70.0	7560	Pave	NaN	Reg	Lvl	AllPub	...	NaN	GdWo	NaN	7	2006	WD	Normal	133700
1054	1055	60	RL	90.0	11367	Pave	NaN	Reg	Lvl	AllPub	...	NaN	NaN	NaN	11	2006	WD	Normal	255000
783	784	85	RL	NaN	9101	Pave	NaN	IR1	Lvl	AllPub	...	NaN	NaN	NaN	7	2009	WD	Normal	165500

	Dwell_Type	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	...	WoodDeckSF	OpenPorchSF	EnclosedPorch	ThrSsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	Property_Sale_Price
count	2073.000000	1753.000000	2073.000000	2073.000000	2073.000000	2073.000000	2073.000000	2059.000000	2073.000000	2073.000000	...	2073.000000	2073.000000	2073.000000	2073.000000	2073.000000	2073.000000	2073.000000	2073.000000	2073.000000	2073.000000
mean	60.556199	72.056475	10717.853353	6.233478	5.835022	1969.445731	1986.617463	101.875668	437.949349	49.219489	...	94.168837	46.035697	21.029908	2.979257	14.552340	2.299083	39.736614	6.287506	2007.851905	180967.284129
std	159.924810	27.987475	9215.982306	1.517115	1.360214	30.222647	20.432694	179.169217	448.051119	165.621465	...	129.072113	64.768713	59.569928	26.817218	54.099626	37.411031	429.819512	2.690130	1.329705	78666.411112
min	20.000000	21.000000	1300.000000	1.000000	1.000000	1872.000000	1950.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	2006.000000	34900.000000
25%	20.000000	60.000000	7620.000000	5.000000	5.000000	1950.000000	1970.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	5.000000	2007.000000	130000.000000
50%	50.000000	70.000000	9492.000000	6.000000	5.000000	1971.000000	1995.000000	0.000000	381.000000	0.000000	...	0.000000	24.000000	0.000000	0.000000	0.000000	0.000000	0.000000	6.000000	2008.000000	163990.000000
75%	70.000000	83.000000	11601.000000	7.000000	7.000000	1999.000000	2004.000000	161.500000	708.000000	0.000000	...	168.000000	67.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.000000	2009.000000	214000.000000
max	7080.000000	313.000000	215245.000000	10.000000	9.000000	2010.000000	2023.000000	1600.000000	5644.000000	1474.000000	...	857.000000	547.000000	552.000000	508.000000	480.000000	738.000000	15500.000000	12.000000	2010.000000	755000.000000

	Zone_Class	Road_Type	Alley	Property_Shape	LandContour	Utilities	LotConfig	LandSlope	Neighborhood	Condition1	...	GarageType	GarageFinish	GarageQual	GarageCond	PavedDrive	PoolQC	Fence	MiscFeature	SaleType	SaleCondition
count	2073	2073	129	2073	2073	2073	2073	2073	2073	2073	...	1960	1960	1960	1960	2073	8	404	80	2073	2073
unique	5	2	2	4	4	2	5	3	25	9	...	6	3	5	5	3	3	4	4	9	6
top	RL	Pave	Grvl	Reg	Lvl	AllPub	Inside	Gtl	NAmes	Norm	...	Attchd	Unf	TA	TA	Y	Gd	MnPrv	Shed	WD	Normal
freq	1611	2063	72	1300	1854	2070	1481	1958	317	1794	...	1252	857	1870	1885	1913	4	233	74	1797	1695

	count	mean	std	min	25%	50%	75%	max
Dwell_Type
20	722.0	180192.246537	66834.894527	35311.0	132000.0	159250.0	217875.0	340000.0
30	134.0	124344.089552	59968.092222	34900.0	86000.0	109700.0	136825.0	340000.0
40	7.0	177600.000000	70304.125057	79500.0	133000.0	152000.0	242850.0	260000.0
45	13.0	110961.538462	21171.306780	76000.0	96500.0	109500.0	132000.0	139400.0
50	205.0	147919.863415	57580.464374	37900.0	114500.0	135000.0	164000.0	340000.0
60	431.0	226878.104408	60349.414027	35311.0	180500.0	214000.0	267500.0	340000.0
70	78.0	170636.205128	55594.426663	40000.0	130500.0	161000.0	195500.0	340000.0
75	19.0	180131.578947	73441.178675	101000.0	123250.0	163000.0	205750.0	340000.0
80	78.0	170040.769231	41372.951945	107000.0	145000.0	165250.0	180000.0	340000.0
85	34.0	146567.647059	18473.006032	123000.0	133225.0	140750.0	154000.0	198500.0
90	91.0	137172.824176	36641.835926	75000.0	118250.0	136500.0	145900.0	268000.0
120	114.0	200466.263158	53764.663148	99500.0	162500.0	192250.0	221750.0	340000.0
150	13.0	190773.076923	71186.924172	127500.0	143750.0	153500.0	236500.0	319900.0
160	78.0	139492.538462	33678.229767	75000.0	115750.0	147200.0	165000.0	239000.0
180	14.0	103785.714286	28187.363703	75500.0	81875.0	91500.0	133250.0	148000.0
190	42.0	130161.904762	32290.716044	55000.0	113000.0	126250.0	143750.0	228950.0

	count	mean	std	min	25%	50%	75%	max
Zone_Class
C (all)	14.0	70135.857143	31906.068856	34900.0	36483.25	74700.0	85000.0	133900.0
FV	102.0	214299.794118	55465.164430	130000.0	173733.00	199900.0	250000.0	340000.0
RH	22.0	134719.727273	31285.377837	76000.0	119900.00	144000.0	145000.0	200000.0
RL	1611.0	186499.616387	65906.057970	39300.0	138000.00	174000.0	224250.0	340000.0
RM	324.0	129303.089506	48130.787063	37900.0	102750.00	122000.0	143000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
Road_Type
Grvl	10.0	119214.300000	51482.429909	55993.0	88250.0	110000.0	116375.0	228950.0
Pave	2063.0	177875.547746	66979.790438	34900.0	130000.0	164000.0	214000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
Alley
Grvl	72.0	119956.305556	34111.276285	52500.0	102250.0	118000.0	139550.0	256000.0
No alley access	1944.0	179800.450103	67683.672106	34900.0	131000.0	165075.0	217125.0	340000.0
Pave	57.0	175095.947368	36880.902953	40000.0	159434.0	174000.0	199900.0	265979.0

	count	mean	std	min	25%	50%	75%	max
Property_Shape
IR1	698.0	200990.607450	67559.667207	52000.0	148500.0	189500.0	239900.0	340000.0
IR2	60.0	214172.233333	64213.328055	110000.0	174000.0	191250.0	248000.0	340000.0
IR3	15.0	203828.333333	62233.650511	73000.0	172250.0	192140.0	237000.0	340000.0
Reg	1300.0	163038.611538	62547.325274	34900.0	120500.0	146750.0	189000.0	340000.0

	df	sum_sq	mean_sq	F	PR(>F)
Property_Shape	3.0	7.481048e+11	2.493683e+11	60.268576	2.371909e-37
Residual	2069.0	8.560729e+12	4.137617e+09	NaN	NaN

	df	sum_sq	mean_sq	F	PR(>F)
Property_Shape	2.0	7.479866e+11	3.739933e+11	90.431012	2.229580e-38
Residual	2070.0	8.560848e+12	4.135675e+09	NaN	NaN

	count	mean	std	min	25%	50%	75%	max
HouseStyle
1.5Fin	214.0	143526.939252	50472.360301	37900.0	115000.00	135000.0	160000.0	340000.0
1.5Unf	16.0	126343.750000	60112.017309	76000.0	101525.00	115500.0	132750.0	340000.0
1Story	1036.0	172477.168919	67414.299476	34900.0	126131.25	154750.0	208900.0	340000.0
2.5Fin	10.0	194050.000000	75491.519023	104000.0	133750.00	194000.0	199250.0	340000.0
2.5Unf	16.0	155609.375000	54983.379970	101000.0	128000.00	133900.0	163250.0	325000.0
2Story	638.0	204188.871473	66991.000845	35311.0	160000.00	190000.0	244400.0	340000.0
SFoyer	54.0	136689.925926	29355.435667	75500.0	129000.00	135480.0	148000.0	206300.0
SLvl	89.0	164525.617978	37307.939380	91000.0	142500.00	160000.0	178000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
MasVnrType
BrkCmn	20.0	144938.550000	41726.240841	89471.0	116375.0	147000.0	163475.0	277000.0
BrkFace	620.0	200255.917742	65695.079837	75000.0	149975.0	183950.0	236000.0	340000.0
None	1247.0	156338.171612	55775.467311	34900.0	119250.0	143500.0	184950.0	340000.0
Stone	186.0	248055.150538	69805.524683	119000.0	194000.0	248900.0	315500.0	340000.0

	count	mean	std	min	25%	50%	75%	max
LandContour
Bnk	92.0	141210.728261	45566.770113	52500.0	111000.0	134950.0	161000.00	315000.0
HLS	76.0	214719.315789	74459.079139	82500.0	157250.0	201000.0	268106.25	340000.0
Low	51.0	197807.843137	81924.079105	39300.0	143000.0	185000.0	267500.00	340000.0
Lvl	1854.0	177319.926106	66201.326059	34900.0	130000.0	164800.0	213187.50	340000.0

	count	mean	std	min	25%	50%	75%	max
LotConfig
Corner	386.0	178871.183938	66358.387741	52500.0	129000.0	167250.0	221500.0	340000.0
CulDSac	137.0	207646.766423	64797.175752	84000.0	149350.0	197500.0	262500.0	340000.0
FR2	65.0	181016.738462	60624.202569	81000.0	148500.0	165000.0	214000.0	340000.0
FR3	4.0	208475.000000	78379.222374	128000.0	169925.0	195450.0	234000.0	315000.0
Inside	1481.0	174245.459824	66997.868261	34900.0	128000.0	160000.0	206900.0	340000.0

	count	mean	std	min	25%	50%	75%	max
LandSlope
Gtl	1958.0	176946.424413	66471.654792	34900.0	130000.0	163000.0	213000.0	340000.0
Mod	101.0	187191.772277	75128.837126	39300.0	130000.0	172000.0	242000.0	340000.0
Sev	14.0	198709.285714	78967.344611	61000.0	147250.0	182500.0	255375.0	340000.0

	count	mean	std	min	25%	50%	75%	max
Neighborhood
Blmngtn	27.0	190835.666667	29284.016787	159895.0	173250.0	181000.0	203845.5	264561.0
Blueste	3.0	133000.000000	15588.457268	124000.0	124000.0	124000.0	137500.0	151000.0
BrDale	17.0	105288.235294	14256.572272	83000.0	91500.0	106000.0	118000.0	125000.0
BrkSide	80.0	125134.687500	42537.203280	39300.0	88750.0	120000.0	142125.0	223500.0
ClearCr	33.0	216852.484848	51130.435689	130000.0	187500.0	200500.0	244400.0	328000.0
CollgCr	217.0	198702.612903	49612.254693	110000.0	150500.0	200000.0	230000.0	340000.0
Crawfor	72.0	210319.597222	62955.783040	90350.0	160000.0	217000.0	250000.0	340000.0
Edwards	138.0	130126.014493	44063.138003	58500.0	103925.0	125000.0	145900.0	320000.0
Gilbert	119.0	190821.268908	29959.671152	141000.0	174500.0	180500.0	193000.0	340000.0
IDOTRR	51.0	99941.215686	33470.887694	34900.0	82750.0	106500.0	120000.0	169500.0
MeadowV	24.0	96362.500000	22172.423383	75000.0	80750.0	88000.0	101500.0	151400.0
Mitchel	78.0	155801.846154	37567.390308	84500.0	131000.0	152200.0	169425.0	271000.0
NAmes	317.0	144819.851735	32532.996994	87500.0	126500.0	140000.0	156500.0	340000.0
NPkVill	17.0	144220.588235	9197.436104	127500.0	143750.0	143750.0	148500.0	155000.0
NWAmes	105.0	191622.476190	36395.292451	82500.0	173000.0	185000.0	207500.0	299800.0
NoRidge	65.0	296817.446154	41736.133024	190000.0	268000.0	302000.0	340000.0	340000.0
NridgHt	99.0	289969.454545	57226.679072	154000.0	254750.0	318000.0	340000.0	340000.0
OldTown	156.0	122497.173077	42776.065932	37900.0	104750.0	118250.0	135000.0	340000.0
SWISU	31.0	143701.096774	32696.637808	60000.0	127500.0	139500.0	164500.0	200000.0
Sawyer	108.0	141572.879630	30697.874546	62383.0	127750.0	135000.0	151750.0	310000.0
SawyerW	93.0	185179.118280	54575.658327	76000.0	144000.0	179200.0	225000.0	320000.0
Somerst	123.0	224583.373984	51250.299541	144152.0	179400.0	225000.0	253000.0	340000.0
StoneBr	32.0	270671.875000	63086.296423	170000.0	213125.0	275000.0	340000.0	340000.0
Timber	53.0	234707.415094	58939.456608	137500.0	187500.0	224500.0	283463.0	340000.0
Veenker	15.0	258100.000000	69359.621848	162500.0	190500.0	270000.0	332000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
Condition1
Artery	62.0	131616.129032	48191.170518	66500.0	105000.0	121450.0	142500.0	340000.0
Feedr	112.0	141863.616071	39016.050482	40000.0	121900.0	141000.0	163000.0	244600.0
Norm	1794.0	180415.495541	67958.906586	34900.0	131000.0	167370.0	219500.0	340000.0
PosA	13.0	242000.000000	59026.830058	180000.0	190000.0	242000.0	250000.0	335000.0
PosN	24.0	209645.833333	61215.803083	109500.0	164625.0	198750.0	264875.0	340000.0
RRAe	15.0	148433.333333	36689.696231	87000.0	132650.0	143900.0	158500.0	256300.0
RRAn	39.0	184348.051282	48809.506292	79500.0	159500.0	178900.0	192140.0	340000.0
RRNe	2.0	190750.000000	5303.300859	187000.0	188875.0	190750.0	192625.0	194500.0
RRNn	12.0	205000.000000	90036.356293	110000.0	110000.0	214000.0	297500.0	320000.0

	count	mean	std	min	25%	50%	75%	max
Condition2
Artery	2.0	106500.000000	16263.455967	95000.0	100750.0	106500.0	112250.0	118000.0
Feedr	9.0	127444.444444	36866.689271	79500.0	85000.0	128000.0	167500.0	167500.0
Norm	2054.0	177839.502434	66936.808031	34900.0	130000.0	164000.0	214000.0	340000.0
PosA	1.0	325000.000000	NaN	325000.0	325000.0	325000.0	325000.0	325000.0
PosN	2.0	262375.000000	109778.327779	184750.0	223562.5	262375.0	301187.5	340000.0
RRAe	1.0	190000.000000	NaN	190000.0	190000.0	190000.0	190000.0	190000.0
RRAn	2.0	136905.000000	0.000000	136905.0	136905.0	136905.0	136905.0	136905.0
RRNn	2.0	96750.000000	39951.533137	68500.0	82625.0	96750.0	110875.0	125000.0

	count	mean	std	min	25%	50%	75%	max
Dwelling_Type
1Fam	1719.0	181941.322862	68783.487306	34900.0	132000.0	169900.0	222500.0	340000.0
2fmCon	44.0	128177.272727	31443.645519	55000.0	113000.0	125000.0	140000.0	228950.0
Duplex	87.0	132676.172414	25563.912932	82000.0	118250.0	136500.0	145000.0	206300.0
Twnhs	55.0	136501.818182	41303.212551	75000.0	95750.0	146000.0	172750.0	230000.0
TwnhsE	168.0	182750.220238	58782.206749	75500.0	143750.0	173733.0	208900.0	340000.0

	count	mean	std	min	25%	50%	75%	max
OverallCond
1	1.0	61000.000000	NaN	61000.0	61000.0	61000.0	61000.0	61000.0
2	5.0	131100.000000	117046.144746	67000.0	76500.0	85000.0	87000.0	340000.0
3	26.0	99367.153846	45847.233272	35311.0	69250.0	87250.0	129250.0	200624.0
4	147.0	163496.204082	68911.443393	37900.0	117500.0	150000.0	192140.0	340000.0
5	943.0	194661.337222	69026.850613	34900.0	142976.5	182900.0	236750.0	340000.0
6	351.0	158068.615385	51991.534275	37900.0	127000.0	144000.0	176000.0	340000.0
7	315.0	168894.031746	61511.613169	79900.0	125250.0	149700.0	199950.0	340000.0
8	172.0	164482.674419	59277.603926	35311.0	125375.0	152700.0	189237.5	340000.0
9	113.0	179425.176991	73785.301835	75000.0	125500.0	160000.0	215000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
RoofStyle
Flat	19.0	198286.315789	66738.692027	82000.0	136000.0	235000.0	249000.0	274970.0
Gable	1606.0	170659.143836	60971.965331	34900.0	128000.0	160000.0	202500.0	340000.0
Gambrel	15.0	167900.000000	66468.209372	40000.0	116200.0	180500.0	207000.0	259500.0
Hip	420.0	203536.702381	81441.325754	55000.0	139825.0	175700.0	275000.0	340000.0
Mansard	11.0	168132.545455	66041.521508	84500.0	117500.0	152000.0	216000.0	265979.0
Shed	2.0	225000.000000	49497.474683	190000.0	207500.0	225000.0	242500.0	260000.0

	count	mean	std	min	25%	50%	75%	max
RoofMatl
ClyTile	1.0	160000.000000	NaN	160000.0	160000.0	160000.0	160000.0	160000.0
CompShg	2036.0	176628.908644	66477.719608	34900.0	130000.0	163000.0	212925.0	340000.0
Membran	1.0	241500.000000	NaN	241500.0	241500.0	241500.0	241500.0	241500.0
Metal	1.0	180000.000000	NaN	180000.0	180000.0	180000.0	180000.0	180000.0
Roll	1.0	137000.000000	NaN	137000.0	137000.0	137000.0	137000.0	137000.0
Tar&Grv	17.0	192702.352941	69462.739880	82000.0	129000.0	185000.0	256000.0	274970.0
WdShake	6.0	249000.000000	37363.083385	190000.0	231500.0	251000.0	280250.0	287000.0
WdShngl	10.0	304450.000000	55839.278290	168500.0	278500.0	340000.0	340000.0	340000.0

Response / Target Variable¶

Property_Sale_Price¶

0) Dwell_Type¶

1) Zone_Class¶

2) LotFrontage¶

3) LotArea¶

4) Road_Type¶

5) Alley¶

6) Property_Shape¶

7) LandContour¶

8) Utilities¶

9) LotConfig¶

10) LandSlope¶

11) Neighborhood¶

12) Condition1¶

13) Condition2¶

14) Dwelling_Type¶

15) HouseStyle¶

16) OverallQual¶

17) OverallCond¶

18) YearBuilt¶

19) YearRemodAdd¶

20) RoofStyle¶

21) RoofMatl¶

22) Exterior1st¶

23) Exterior2nd¶

24) MasVnrType¶

25) MasVnrArea¶

26) ExterQual¶

27) ExterCond¶

28) Foundation¶

29) BsmtQual¶

30) BsmtCond¶

31) BsmtExposure¶

32) BsmtFinType1¶

33) BsmtFinSF1¶

34) BsmtFinType2¶

35) BsmtFinSF2¶

36) BsmtUnfSF¶

37) TotalBsmtSF¶

38) Heating¶

39) HeatingQC¶

40) CentralAir¶

41) Electrical¶

42) FirstFlrSF¶

43) SecondFlrSF¶

44) LowQualFinSF¶

45) GrLivArea¶

46) BsmtFullBath¶

47) BsmtHalfBath¶

	count	mean	std	min	25%	50%	75%	max
Exterior1st
AsbShng	24.0	103680.541667	38013.525554	35311.0	81625.0	108000.0	135000.0	165500.0
AsphShn	2.0	100000.000000	0.000000	100000.0	100000.0	100000.0	100000.0	100000.0
BrkComm	3.0	74666.666667	12701.705922	60000.0	71000.0	82000.0	82000.0	82000.0
BrkFace	72.0	187479.861111	75316.777333	40000.0	138500.0	160000.0	246625.0	340000.0
CBlock	1.0	105000.000000	NaN	105000.0	105000.0	105000.0	105000.0	105000.0
CemntBd	87.0	214235.344828	93453.373330	75000.0	117250.0	229456.0	291045.0	340000.0
HdBoard	316.0	158908.348101	44061.480945	83000.0	130000.0	147750.0	179200.0	340000.0
ImStucc	1.0	262000.000000	NaN	262000.0	262000.0	262000.0	262000.0	262000.0
MetalSd	308.0	150353.814935	53640.877044	62383.0	119000.0	140000.0	167125.0	340000.0
Plywood	156.0	172811.660256	48213.268088	82500.0	143187.5	164250.0	197500.0	340000.0
Stone	3.0	268000.000000	32908.965344	230000.0	258500.0	287000.0	287000.0	287000.0
Stucco	28.0	161579.607143	74703.555441	58500.0	111500.0	146500.0	191550.0	340000.0
VinylSd	750.0	208089.890667	66237.103227	37900.0	162000.0	199900.0	248757.0	340000.0
Wd Sdng	285.0	147160.978947	57668.328575	34900.0	110000.0	135000.0	169000.0	340000.0
WdShing	37.0	159834.648649	78941.789121	87000.0	115000.0	129900.0	172500.0	340000.0

	count	mean	std	min	25%	50%	75%	max
Exterior2nd
AsbShng	27.0	110716.037037	42612.862368	35311.0	90000.0	122500.0	135000.0	225000.0
AsphShn	4.0	128500.000000	36041.642582	100000.0	100000.0	119500.0	148000.0	175000.0
Brk Cmn	11.0	130363.636364	36381.376355	60000.0	114000.0	148500.0	155000.0	155000.0
BrkFace	35.0	193470.000000	76279.933565	40000.0	147500.0	160000.0	250000.0	340000.0
CBlock	1.0	105000.000000	NaN	105000.0	105000.0	105000.0	105000.0	105000.0
CmentBd	85.0	213299.705882	93417.583395	75000.0	115000.0	229456.0	287090.0	340000.0
HdBoard	292.0	165542.595890	49759.903194	83000.0	131475.0	157000.0	185000.0	340000.0
ImStucc	14.0	230692.857143	97939.608270	88000.0	135000.0	229000.0	320000.0	340000.0
MetalSd	299.0	150934.698997	53912.357252	62383.0	119000.0	140000.0	166000.0	340000.0
Other	1.0	319000.000000	NaN	319000.0	319000.0	319000.0	319000.0	319000.0
Plywood	205.0	165383.175610	46085.008061	76500.0	134432.0	154000.0	193000.0	320000.0
Stone	9.0	128291.555556	57265.806043	90000.0	90000.0	93500.0	177000.0	230000.0
Stucco	29.0	156310.793103	70876.371844	58500.0	90350.0	144000.0	200100.0	340000.0
VinylSd	729.0	208688.524005	65998.120466	37900.0	163990.0	200000.0	248900.0	340000.0
Wd Sdng	277.0	146030.523466	56313.471191	34900.0	110000.0	135000.0	164900.0	340000.0
Wd Shng	55.0	163871.818182	75437.407684	55000.0	120000.0	137450.0	173000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
ExterQual
Ex	76.0	304193.276316	55255.814100	160000.0	299800.0	335000.0	340000.0	340000.0
Fa	19.0	90936.473684	34933.709741	39300.0	71000.0	85000.0	110000.0	200000.0
Gd	706.0	227613.082153	56929.057167	52000.0	184900.0	221250.0	265979.0	340000.0
TA	1272.0	143559.811321	41343.779478	34900.0	118768.5	139000.0	165000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
ExterCond
Ex	3.0	201333.333333	109235.220205	118000.0	139500.0	161000.0	243000.0	325000.0
Fa	35.0	106946.514286	38633.905585	39300.0	80500.0	115000.0	140000.0	169500.0
Gd	219.0	170332.031963	58812.747429	68400.0	130750.0	158000.0	201000.0	340000.0
Po	1.0	76500.000000	NaN	76500.0	76500.0	76500.0	76500.0	76500.0
TA	1815.0	179847.413223	67531.729210	34900.0	130500.0	167000.0	216250.0	340000.0

	count	mean	std	min	25%	50%	75%	max
Foundation
BrkTil	191.0	129701.293194	48595.423104	37900.0	100000.0	124900.0	154400.00	340000.0
CBlock	901.0	149323.705882	46434.127889	34900.0	123000.0	141000.0	167900.00	340000.0
PConc	928.0	217604.738147	65636.968941	78000.0	173733.0	204750.0	260675.00	340000.0
Slab	40.0	110771.525000	31139.767500	39300.0	90000.0	108700.0	124123.00	198500.0
Stone	10.0	191073.400000	79767.802738	102776.0	116000.0	201489.5	266369.75	266500.0
Wood	3.0	185666.666667	56695.090910	143000.0	153500.0	164000.0	207000.00	250000.0

	count	mean	std	min	25%	50%	75%	max
BsmtQual
Ex	169.0	289784.644970	59455.881416	123500.0	240000.0	318000.0	340000.0	340000.0
Fa	50.0	119061.420000	36058.605421	61000.0	91875.0	114000.0	134250.0	206900.0
Gd	904.0	201837.722345	54558.954794	75500.0	166830.0	192000.0	235346.0	340000.0
No Basement	59.0	106993.949153	28115.764591	39300.0	90000.0	101800.0	118964.0	198500.0
TA	891.0	139673.151515	41221.918687	34900.0	116700.0	135000.0	156500.0	340000.0

	count	mean	std	min	25%	50%	75%	max
BsmtCond
Fa	61.0	125764.885246	43955.279142	55000.0	96500.0	119200.0	147000.0	265979.0
Gd	100.0	213821.820000	60761.330363	119000.0	165750.0	199000.0	266500.0	340000.0
No basement	59.0	106993.949153	28115.764591	39300.0	90000.0	101800.0	118964.0	198500.0
Po	2.0	64000.000000	4242.640687	61000.0	62500.0	64000.0	65500.0	67000.0
TA	1851.0	179716.323609	66406.658696	34900.0	132500.0	165400.0	215000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
BsmtExposure
Av	322.0	199222.850932	70246.764118	35311.0	144125.00	185675.0	246578.0	340000.0
Gd	181.0	234769.093923	78900.744173	61000.0	167900.00	225000.0	315500.0	340000.0
Mn	160.0	193995.568750	69158.528027	78000.0	142093.75	186750.0	239799.0	340000.0
No	1349.0	165878.057821	57988.554630	34900.0	125500.00	155000.0	193000.0	340000.0
No Basement	61.0	109797.426230	31620.647693	39300.0	90000.00	106250.0	120000.0	198500.0

	count	mean	std	min	25%	50%	75%	max
BsmtFinType1
ALQ	319.0	160272.648903	44780.348164	81000.0	129250.0	148500.0	179400.0	340000.0
BLQ	210.0	147228.252381	44001.875087	35311.0	122000.0	140000.0	161500.0	340000.0
GLQ	599.0	225608.691152	68004.823134	80000.0	173733.0	214000.0	275000.0	340000.0
LwQ	109.0	149950.000000	48729.133711	75500.0	117000.0	139000.0	172500.0	301000.0
No basement	59.0	106993.949153	28115.764591	39300.0	90000.0	101800.0	118964.0	198500.0
Rec	173.0	147012.254335	42258.300502	34900.0	125000.0	143000.0	161000.0	340000.0
Unf	604.0	170322.137417	66293.801295	37900.0	118500.0	165000.0	207000.0	340000.0

	count	mean	std	min	25%	50%	75%	max
BsmtFinType2
ALQ	24.0	199154.166667	73038.304299	123500.0	131000.0	169700.0	290000.0	340000.0
BLQ	50.0	146091.660000	36211.174172	62383.0	129000.0	141000.0	158712.5	271900.0
GLQ	22.0	181625.000000	63255.799188	75500.0	114250.0	193000.0	236500.0	270000.0
LwQ	68.0	167181.617647	43912.261491	88000.0	138500.0	155500.0	189725.0	287000.0
No Basement	60.0	109944.050000	36045.577789	39300.0	90000.0	104025.0	119223.0	284000.0
Rec	79.0	161127.531646	48851.882623	85000.0	129000.0	147000.0	178450.0	340000.0
Unf	1770.0	181567.964407	68304.963259	34900.0	132500.0	169250.0	221000.0	340000.0